Evaluating the structure of enemy biodiversity effects on prey in agroecosystems can provide insights into biological pest control functioning. With this aim, theoretical models that describe biological mechanisms underlying prey suppression can be developed and confronted with experimental data by means of model selection. Here, we confront multiplicative risk models to evaluate the structure of multiple predator effects on the whitefly
A key challenge in ecology is to determine how changes in biodiversity affect ecosystem functioning and ecosystem service provision [
To date, attempts to address the complexity underlying enemy biodiversity effects on prey have led to varied experimental approaches to partition individual mechanisms underlying prey suppression (e.g., [
With this aim, we extend and adapt multiplicative risk models [
Overall, this paper attempts to show how process modeling can be useful to capture information contained in available data and inform pest management with a set of plausible hypotheses about pest control functioning in given local systems.
Experiments were performed during Summer 2006 in a fresh-market tomato crop (variety Empire, Petoseed) situated in south-east Sardinia, Italy (NW corner — UTM: 32S 0524770; 4349400). In this area, the greenhouse whitefly
The effect of single-predator (
The experimental field was settled and managed by a local farmer (Claudio Orrù) in his own farm under the supervision of the authors. Two thousand tomato seedlings were transplanted at the beginning of June. Approximately one month later, 66 experimental units (tomato plants) were randomly selected and caged in mesh net-bags (0.5 mm mesh, 100 cm tall, and 40 cm in diameter) secured to the ground. Before cage closure, plants were treated with pyrethrum and visually inspected to eliminate arthropods. Five days after the closure of mesh net-bags, about 80 whitefly adults were introduced in each cage to start the whitefly population with egg deposition. Whitefly adults were collected 1 day before their release from an organic vegetable garden, managed by Paolo Casula and situated a few kilometers away, and stored at 5°C. At the beginning of whitefly adult emergence, which started approximately 20 days after whitefly introduction and egg deposition (July 25th and August 2nd for blocks A and B, resp.), predators were released and kept in experimental units for 6 days. In this way, predation on both the whitefly nymph and the adult stage could be studied.
To keep starting densities of single-and-multiple predator treatments within ranges commonly found in this ecological system [
At the end of the experiment, cages were opened, and insects recovered from plants by means of a suction sampler (Vortis, Burkard Manufacturing Ltd.) and careful visual inspection. Insects were stored in alcohol and subsequently counted by means of a stereomicroscope. The number of whitefly adults (
Additionally, samples of up to 12 infested leaves per experimental unit were observed under a stereomicroscope to count the number of nymphs dead and alive and pupal cases from which adult whiteflies emerged (
The starting point of model selection approaches is to develop a set of
When dealing with predation, attempts to develop realistic process models can start from multiplicative risk framework [
If the number of whiteflies surviving predation from a given predator species
As often happens in field studies, not everything could be controlled; that is, starting whitefly nymph densities (see Table
In order to model possible variations of per capita predation rate due to interactions between predators, per capita predation rates of each predator species when released in single- or in two-predator treatments were estimated separately as follows:
Finally, two parameters representing unknown mortality of the whitefly adults (
In the previous section, we developed a general model embedding the main factors thought to affect variation in the data. During confrontation with data, the general model is simplified to search for parsimonious models actually supported by the data, achieved here by means of model selection based on Akaike’s Information Criterion (AIC) [
Starting from the general model described above, simplified models were developed by constraining or eliminating a single factor at a time. Factors were excluded if model simplification resulted in lower AIC values. If AIC values were higher, similar, or with
The first part of the process—whitefly nymph survival—was studied by analysing the series of counts
Best model structure and main parameter estimates relative to enemy biodiversity effects on whitefly nymph survival.
Code | Model structure | Model selection diagnostics | Maximum likelihood estimates of model parameters | |||||||||||||
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ΔQAICc |
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S1 |
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57.6253 | 0.0000 | 27 | 2.3675 | 1.0000 | 0.0000 | 0.0729 | 0.3004 | 0.0000 | 0.0000 | 0.4914 | 0.1611 | 0.0256 | 0.1192 | 0.2908 |
S2 |
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34.4510 | 0.0000 | 21 | 2.2286 | 0.9944 | 0.0005 | 0.0869 | 0.2860 | 0.4184 | 0.2474 | 0.4899 | 0.1097 | 0.1541 | 0.1787 | 0.1552 |
S3 |
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14.2799 | 0.0004 | 15 | 1.8687 | 0.9810 | 0.0015 | 0.0894 | 0.3128 | 0.3905 | 0.2870 | 0.5029 | 0.1163 | 0.1633 | 0.1802 | 0.1700 |
S4 |
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11.5475 | 0.0014 | 14 | 1.8225 | 0.9787 | 0.0017 | 0.0914 | 0.3001 | 0.3798 | 0.2615 | 0.4784 | 0.1148 | 0.1614 | 0.1785 | 0.1678 |
S5 |
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26.3280 | 0.0000 | 13 | 1.6959 | 0.9072 | 0.0075 | 0.0906 | 0.1021 | 0.1645 | 0.0850 | 0.1304 | 0.1336 | 0.2037 | 0.2306 | 0.1646 |
S6 |
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24.1824 | 0.0000 | 13 | 1.4922 | 0.8872 | 0.0092 | — | 0.2716 | 0.2591 | 0.2200 | 0.3759 | 0.1208 | 0.1598 | 0.1878 | 0.1662 |
S7 |
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8.3366 | 0.0070 | 13 | 1.7840 | 0.9783 | 0.0018 | 0.0914 | 0.3019 | = |
0.2616 | 0.4788 | 0.1159 | 0.1633 | 0.1804 | 0.1739 |
S8 |
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5.3157 | 0.0317 | 12 | 1.7565 | 0.9785 | 0.0017 | 0.0917 | 0.2976 | = |
= |
0.5006 | 0.1162 | 0.1638 | 0.1809 | 0.1757 |
S9 |
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5.1225 | 0.0350 | 11 | 1.7795 | 0.9759 | 0.0020 | 0.0917 | 0.3323 | = |
= |
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0.1152 | 0.1619 | 0.1792 | 0.1704 |
S10 |
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8.1180 | 0.0078 | 11 | 1.6109 | 0.9515 | 0.0039 | 0.0931 | 0.2973 | = |
= |
0.5001 | 0.1189 | = |
0.1588 | 0.1520 |
S11 |
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10.5181 | 0.0024 | 11 | 1.6521 | 0.9478 | 0.0042 | 0.1006 | 0.3013 | = |
= |
0.5203 | 0.1307 | 0.1590 | = |
0.1711 |
S12 |
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9.2109 | 0.0045 | 11 | 1.5667 | 0.9461 | 0.0044 | 0.0927 | 0.2874 | = |
= |
0.4388 | 0.1238 | 0.1494 | 0.1684 | = |
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0.0244 | 0.4473 | 10 | 1.7054 | 0.9780 | 0.0018 | 0.0928 | 0.2974 | = |
= |
0.4972 | 0.1180 | 0.1750 | = |
= |
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0.0000 | 0.4528 | 9 | 1.7292 | 0.9755 | 0.0020 | 0.0929 | 0.3338 | = |
= |
= |
0.1171 | 0.1722 | = |
= |
S15 |
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7.6905 | 0.0097 | 8 | 1.5302 | 0.9224 | 0.0063 | 0.0981 | 0.3221 | = |
= |
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0.1244 | = |
= |
= |
S16 |
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25.7016 | 0.0000 | 8 | 1.4843 | 0.0000 | 0.0151 | 0.0899 | 0.3239 | = |
= |
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0.0852 | 0.1631 | = |
= |
S17 |
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57.8675 | 0.0000 | 3 | 1.1455 | 0.0000 | 0.0813 | 0.2412 | — | — | — | — | — | — | — | — |
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The general model S1,
The second part of the process—whitefly adult emergence—was studied by analysing the series of counts
The third part of the process—whitefly adult survival—was studied by analysing the series of counts
Best model structure and main parameter estimates relative to enemy biodiversity effects on whitefly nymph survival, adult emergence and survival (joint analysis).
Code | Model structure | Model selection diagnostics | Maximum likelihood estimates of model parameters | ||||||||||||||
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ΔQAICc |
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J1 |
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64.6797 | 0.000 | 39 | 1.5351 | 0.1166 | 0.2077 | 0.4466 | 0.2272 | 0.2720 | 0.0000 | 0.1750 | 0.0000 | 0.5198 | 0.0000 | 0.0000 | 0.0000 |
J2 |
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61.5743 | 0.000 | 38 | 1.5255 | 0.1166 | 0.2077 | 0.4466 | 0.2272 | 0.2720 | 0.0000 | 0.1750 | 0.0000 | 0.5198 | 0.0000 | 0.0000 | 0.0000 |
J3 |
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38.1612 | 0.000 | 30 | 1.4528 | 0.1166 | 0.2077 | 0.4473 | 0.2269 | 0.2725 | 0.0000 | 0.1751 | 0.0000 | 0.2745 | 0.0152 | 0.0000 | 0.0000 |
J4 |
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27.2339 | 0.000 | 26 | 1.4190 | 0.1166 | 0.2077 | 0.4473 | 0.2269 | 0.2725 | 0.0000 | 0.0962 | 0.0000 | 0.2745 | 0.0815 | 0.0873 | 0.0000 |
J5 |
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16.8063 | 0.000 | 22 | 1.3867 | 0.1166 | 0.2077 | 0.4473 | 0.2269 | 0.2725 | 0.0000 | 0.0918 | 0.0000 | 0.2745 | 0.0815 | 0.0918 | 0.0000 |
J6 |
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5.6330 | 0.016 | 16 | 1.3440 | 0.0109 | 0.2326 | 0.4310 | 0.2234 | 0.0000 | = |
= |
= |
0.0498 | = |
= |
= |
J7 |
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3.2648 | 0.051 | 15 | 1.3367 | 0.0109 | 0.2326 | 0.4310 | 0.2234 | — | — | — | — | 0.0498 | = |
= |
= |
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0.3907 | 0.216 | 15 | 1.3350 | 0.0886 | 0.2143 | 0.4411 | 0.2249 | 0.2642 | — | — | — | = |
— | — | — |
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0.0000 | 0.263 | 15 | 1.3351 | 0.1037 | 0.2108 | 0.4443 | 0.2266 | 0.2395 | — | — | — | = |
— | = |
— |
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1.0172 | 0.158 | 15 | 1.3362 | 0.0734 | 0.2179 | 0.4394 | 0.2258 | 0.1484 | — | = |
— | = |
— | = |
— |
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1.0771 | 0.153 | 14 | 1.3297 | 0.0000 | 0.2352 | 0.4275 | 0.2223 | — | — | — | — | — | — | — | — |
J12 |
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1.2322 | 0.142 | 14 | 1.3455 | 0.1060 | 0.2102 | 0.4441 | 0.2264 | 0.2389 | — | — | — | = |
— | = |
— |
J13 |
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3.4194 | 0.045 | 15 | 1.3370 | 0.0000 | 0.2352 | 0.4275 |
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— | — | — | — | — | — | — | — |
J14 |
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45.7700 | 0.000 | 14 | 1.3063 | 0.3911 | 0.3344 | = |
0.2036 | — | — | — | = |
— | = |
— | |
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ML estimates of parameters relative to predation on nymphs for Model J9 | |||||||||||||||||
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−0.0892 | 0.0935 | 0.0019 | 0.0015 | 0.0076 | 0.2563 | = |
= |
0.4286 | 0.1137 | 0.1686 | = |
= |
The general model J1,
Events resulting in whitefly nymph mortality, adult emergence, and adult mortality can be seen as a sequence of independent processes acting simultaneously. In these cases, likelihood theory can incorporate multiple sources of observations; that is, the likelihood of the joint model is the product of the likelihood of each series of data [
The three series of counts (
There is another source of variation possibly affecting the process and the experimental outcomes: the number of predators actually preying on whiteflies, which could be reduced by intraguild predation. To address this possible variation, the exponents
The low number of predators recovered at the end of the experiment could be due to predator background mortality, detectability lower than 1 [
The average numbers of predators recovered at the end of the experiment for each species and treatment were thus embedded in all models presented in Tables
The negative binomial distribution, suitable for analysis of counts [
Maximum likelihood estimation of model parameters was achieved by numerical search [
To address for possible overdispersion, very frequent in real insect count data, model selection was achieved through the calculation of quasi-likelihood adjustments for overdispersion (
Finally, support for each model
Table
Mean mortality rates of whitefly nymphs estimated for each treatment are shown in Table
Mean mortality of whitefly nymphs (
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0.4187 | |||
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0.4016 | 0.2562 | ||
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0.4009 | 0.2009 | 0.0673 | |
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0.4159 | 0.2438 | 0.1042 | 0.0792 |
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C | 0.0978 |
Mean number of predators recovered in each treatment (±SD;
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Mean | 7.00 | 5.63 | 0.96 | 1.42 |
Number of predators recovered for each experimental unit are given in Table
Number of whitefly nymphs, adults and predators counted at the end of the experiment and model expectations.
Treatment | Block | Counts in Experimental Units (EU) | Counts in Leaf Samples (LS) | Model expectations | ||||||||||
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Whiteflies | Predators | Whitefly nymphs | LS | EU | ||||||||||
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C | A | 956 |
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1 | 0 | 0 | 0 | 43 | 154 |
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369 |
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334 | 385 |
C | A | 830 |
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3 | 0 | 0 | 0 | 11 | 235 |
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372 |
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337 | 334 |
C | A | 937 |
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0 | 0 | 0 | 0 | 30 | 156 |
|
409 |
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371 | 377 |
C | B | 280 |
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0 | 0 | 0 | 0 | 17 | 45 |
|
96 |
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87 | 58 |
C | B | 480 |
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2 | 0 | 0 | 0 | 36 | 129 |
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237 |
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215 | 99 |
C | B | 530 |
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0 | 0 | 0 | 0 | 7 | 115 |
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183 |
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166 | 109 |
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A | 670 |
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0 | 0 | 2 | 0 | 38 | 221 |
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329 |
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298 | 205 |
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A | 707 |
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0 | 0 | 1 | 0 | 17 | 127 |
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253 |
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229 | 217 |
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A | 911 |
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0 | 0 | 1 | 0 | 17 | 132 |
|
289 |
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262 | 279 |
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B | 910 |
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2 | 0 | 3 | 0 | 22 | 179 |
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251 |
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228 | 142 |
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B | 350 |
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1 | 0 | 1 | 0 | 16 | 101 |
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155 |
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141 | 55 |
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B | 550 |
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1 | 0 | 2 | 0 | 7 | 114 |
|
164 |
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149 | 86 |
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A | 405 |
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2 | 0 | 2 | 7 | 33 | 124 |
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242 |
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200 | 149 |
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A | 501 |
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0 | 0 | 1 | 7 | 8 | 104 |
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307 |
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266 | 193 |
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A | 300 |
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0 | 0 | 1 | 2 | 48 | 60 |
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154 |
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114 | 99 |
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B | 810 |
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0 | 0 | 2 | 4 | 16 | 202 |
|
285 |
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257 | 165 |
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B | 150 |
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0 | 0 | 0 | 9 | 48 | 15 |
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76 |
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30 | 13 |
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B | 340 |
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0 | 0 | 3 | 5 | 43 | 69 |
|
143 |
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107 | 58 |
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A | 1030 |
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0 | 1 | 2 | 0 | 23 | 195 |
|
429 |
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389 | 315 |
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A | 717 |
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0 | 1 | 0 | 0 | 48 | 185 |
|
367 |
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333 | 220 |
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A | 510 |
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0 | 1 | 1 | 0 | 23 | 134 |
|
240 |
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218 | 156 |
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B | 30 |
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0 | 0 | 0 | 0 | 0 | 9 |
|
13 |
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12 | 5 |
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B | 190 |
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1 | 0 | 3 | 0 | 35 | 79 |
|
157 |
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142 | 30 |
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B | 530 |
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1 | 0 | 1 | 0 | 20 | 107 |
|
164 |
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149 | 83 |
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A | 406 |
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0 | 0 | 0 | 5 | 19 | 107 |
|
171 |
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148 | 156 |
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A | 368 |
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0 | 0 | 1 | 5 | 24 | 64 |
|
172 |
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146 | 139 |
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A | 865 |
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0 | 0 | 0 | 7 | 32 | 159 |
|
333 |
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301 | 348 |
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B | 380 |
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0 | 0 | 0 | 5 | 36 | 107 |
|
187 |
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155 | 71 |
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B | 70 |
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2 | 0 | 0 | 7 | 33 | 12 |
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46 |
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15 | 5 |
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B | 290 |
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0 | 0 | 0 | 8 | 39 | 59 |
|
139 |
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105 | 50 |
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A | 540 |
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3 | 0 | 0 | 3 | 118 | 115 |
|
330 |
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242 | 176 |
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A | 717 |
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5 | 0 | 0 | 6 | 26 | 136 |
|
245 |
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190 | 247 |
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A | 340 |
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6 | 0 | 0 | 3 | 87 | 38 |
|
194 |
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128 | 100 |
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B | 260 |
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7 | 0 | 0 | 1 | 105 | 34 |
|
178 |
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72 | 24 |
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B | 370 |
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0 | 0 | 0 | 2 | 81 | 52 |
|
179 |
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87 | 41 |
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B | 520 |
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12 | 0 | 0 | 6 | 106 | 94 |
|
233 |
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132 | 67 |
|
A | 406 |
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0 | 2 | 0 | 5 | 64 | 177 |
|
323 |
|
277 | 155 |
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A | 513 |
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1 | 1 | 0 | 8 | 62 | 86 |
|
230 |
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203 | 202 |
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A | 1776 |
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0 | 0 | 0 | 5 | 45 | 164 |
|
401 |
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363 | 715 |
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B | 1230 |
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8 | 2 | 0 | 8 | 25 | 172 |
|
258 |
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234 | 253 |
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B | 250 |
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0 | 1 | 0 | 5 | 38 | 68 |
|
155 |
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106 | 39 |
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B | 280 |
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5 | 2 | 0 | 12 | 40 | 56 |
|
141 |
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102 | 46 |
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A | 213 |
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3 | 0 | 0 | 0 | 31 | 66 |
|
164 |
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127 | 73 |
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A | 466 |
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11 | 0 | 0 | 0 | 45 | 105 |
|
198 |
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161 | 168 |
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A | 390 |
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11 | 0 | 0 | 0 | 59 | 115 |
|
317 |
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254 | 139 |
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B | 130 |
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13 | 0 | 0 | 0 | 62 | 11 |
|
74 |
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28 | 11 |
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B | 320 |
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15 | 0 | 0 | 0 | 94 | 25 |
|
153 |
|
72 | 34 |
|
B | 230 |
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0 | 0 | 0 | 0 | 70 | 44 |
|
153 |
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66 | 22 |
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A | 510 |
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7 | 0 | 0 | 0 | 40 | 85 |
|
235 |
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171 | 165 |
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A | 685 |
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12 | 0 | 4 | 0 | 94 | 91 |
|
310 |
|
238 | 233 |
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A | 832 |
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4 | 0 | 1 | 0 | 72 | 92 |
|
228 |
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181 | 293 |
|
B | 400 |
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7 | 0 | 1 | 0 | 113 | 19 |
|
142 |
|
74 | 47 |
|
B | 170 |
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2 | 0 | 2 | 0 | 68 | 44 |
|
115 |
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47 | 16 |
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B | 430 |
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2 | 0 | 0 | 0 | 67 | 97 |
|
210 |
|
112 | 52 |
|
A | 271 |
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6 | 3 | 0 | 0 | 80 | 59 |
|
204 |
|
119 | 70 |
|
A | 1208 |
|
4 | 1 | 0 | 0 | 65 | 158 |
|
327 |
|
266 | 436 |
|
A | 823 |
|
9 | 1 | 0 | 0 | 124 | 155 |
|
394 |
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294 | 273 |
|
B | 670 |
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8 | 1 | 0 | 0 | 77 | 83 |
|
189 |
|
101 | 81 |
|
B | 600 |
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16 | 1 | 0 | 0 | 110 | 83 |
|
229 |
|
116 | 69 |
|
B | 310 |
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5 | 1 | 0 | 0 | 101 | 34 |
|
165 |
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60 | 26 |
|
A | 364 |
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0 | 1 | 0 | 0 | 15 | 158 |
|
227 |
|
206 | 111 |
|
A | 970 |
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1 | 1 | 0 | 0 | 22 | 190 |
|
330 |
|
299 | 297 |
|
A | 467 |
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0 | 1 | 0 | 0 | 19 | 93 |
|
258 |
|
234 | 143 |
|
B | 1010 |
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3 | 0 | 0 | 0 | 12 | 215 |
|
287 |
|
260 | 158 |
|
B | 410 |
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1 | 0 | 0 | 0 | 22 | 95 |
|
184 |
|
167 | 64 |
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B | 640 |
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1 | 1 | 0 | 0 | 6 | 91 |
|
166 |
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150 | 100 |
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Averages | A | 655 |
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45 | 128 |
|
283 |
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239 | 229 | ||||
B | 427 |
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46 | 82 |
|
167 |
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121 | 71 |
Table
Figure
Confrontation of number of whitefly nymphs survived in each leaf sample (
The other models presented in Table
Table
Figure
Confrontation of number of whitefly adults counted in each experimental unit (
In fact, the other three models sharing similar support state that
On the other hand, it is important to note that the very high
The well supported model parameters relative to mirid predation on whitefly nymphs provide some interesting information about possible functional differences on the two species investigated here. By using predation rates taken from model J9, Figure
Model predictions about variation of per capita predation rates of mirids with species identity, whitefly nymph density and interactions between predators. Per capita predation rates of
As described in the methods section, all models ranked in Table
This study, performed in a relatively simplified experimental system, suggests several dimensions across which whitefly control is affected by natural enemies in tomatoes. We will briefly discuss below uncertainty and relevance of observed effects for pest control.
In general, predator-specific effectiveness related to identity and complementarity across prey life stages are well-known dimensions of predator biodiversity effects on prey [
Ecological interactions between predators [
On the opposite, prey risk enhancement is well supported by the constant increase of the estimated per capita predation rate of
In general, knowledge about functional response curves of predators is important in order to properly describe multiple predator effects [
Results showed block effects on emergence rates of whiteflies and on per capita predation rates of predatory Miridae on whitefly nymphs, likely due to the observed variation of environmental temperature between temporal blocks (Block A:
From a general enemy biodiversity and pest control perspective, this study suggests that the predator species evaluated here have complementary roles in whitefly control. The two species of predatory Miridae are both very effective on whitefly nymphs but showed different functional responses and effectiveness with different prey densities. Also, the mirids seem unable to prey on whitefly adults. The spiders, by weakly preying on adults, may contribute a small but important component of total whitefly suppression, with particular reference to the beginning of the season when whiteflies colonize crop fields and prey densities are low. Moreover, as multiple predator treatments did not show a reduction of whitefly control, spiders do not appear to interfere considerably with the predatory Miridae. A reduced relevance of intraguild predation for mirid population growth and whitefly control is also confirmed by the very high densities generally reached by predatory Miridae in untreated and enemy-rich tomatoes [
Therefore, even from a limited perspective of single-prey suppression and short term experiment, and by considering only pair-wise interactions, assembly of the four predator species appears here rather like an accumulation of component parts of biological control function, with predator complementarity arising across prey life stages and density and the emergence of weak interactions between predators that did not result in whitefly control disruption. These results suggest that increasing enemy biodiversity and whitefly control in tomatoes can be compatible and related goals [
By providing a structural description of the roles that different natural enemies have on whitefly suppression, this study informs us about biological control of whiteflies in tomatoes. As the pest control functioning of agroecosystems is affected by locally different biological communities and ecological conditions, site- or regional-specific information about agroecosystem composition, structure, and functioning is needed to foster sustainable agriculture [
Many thanks are due to Andy Wilby, Matt Thomas, Kai Lorenzen, Jay Rosenheim, Simon Leather, Tim Benton, and three anonymous referees for their comments on the paper, to Lia and Dina Raggio for kindly providing help with mesh-bags, and to Claudio Orrù for the careful management of the experimental field. Thanks also are due to Regione Autonoma della Sardegna and the Dina and Franco’s Foundation for the economic and logistic support (Grant L.R. 22/04/2002, n. 7, Art. 25, comma 12).