Type of contribution: Research Paper
Names of authors: Belén Cotesa, Mercedes Camposa, Pedro A. Garcíac, Felipe Pascualb,
Francisca Ruanob
Affiliations: aDepartment of Environmental Protection, Estación Experimental del Zaidín
(CSIC) Profesor Albareda 1, 18008 Granada, Spain.
b
Department of Animal Biology and cDepartment of Statistics & O.R., University of
Granada. Campus de Fuentenueva s/n 18071–Granada, Spain.
Type of contribution: Research paper
Number of Tables: 3
Number of Figures: 2
Title: Testing the suitability of the taxonomic level order of insects as indicators for olive
farming systems
Keywords: hemeroby and Shannon index, Non–parametric Linear Discriminant Analysis
(NPLDA), preblooming and postblooming period.
Names of authors: Belén Cotesa, Mercedes Camposa, Pedro A. Garcíac, Felipe Pascualb,
Francisca Ruanob
Affiliations: aDepartment of Environmental Protection, Estación Experimental del Zaidín

Corresponding author: Department of Environmental Protection. Estación Experimental
del Zaidín. Profesor Albareda, 1. 18008, Granada. Spain. Telephone: +34 958 181600
Fax: +34 958 129600; e-mail: belen.cotes@eez.csic.es
1
(CSIC) Profesor Albareda 1, 18008 Granada, Spain.
b
Department of Animal Biology and cDepartment of Statistics & O.R., University of
Granada. Campus de Fuentenueva s/n 18071–Granada, Spain.
2
1
Abstract
2
1. A previous study suggested the use of certain insects groups as indicators for
3
detecting organic olive farming in southern Spain. To validate the use of those
4
groups, insects were collected from olive orchards in two provinces, Cordoba
5
and Granada, in Andalusia with different surrounding landscapes.
6
2. Canopies were sampled using the branch–beating technique during pre–
7
blooming and post–blooming periods over three years in Granada (1999, 2000
8
and 2003) and one year in Cordoba (2003).
9
3. Using a Non–parametric Linear Discriminant Analysis (NPLDA) method, based
10
on the k–Nearest Neighbour (kNN) algorithm, two discriminant functions were
11
constructed. A first discriminant model took into account interannual variability
12
in the Granada province and the second model focused on environmental
13
heterogeneity between the two provinces. Cross–validation techniques, such as
14
leave–one–out (LOO) and split–sample, were applied to the associated
15
discriminant functions for each model to check their performance.
16
4. Even though differences existed in the insect composition between the regions,
17
the second model correctly classified 78.1% of the sampled blocks under the
18
non–organic and organic farming systems while taking into account two orders:
19
Coleoptera and Hemiptera (excluding E. olivina species and the Heteroptera
20
suborder). Results suggest that the relative abundance of these groups, in the
21
post–blooming period, might constitute a potential bio–indicator of organic
22
olive farming system.
23
Keywords: hemeroby and Shannon index, Non–parametric Linear Discriminant Analysis
24
(NPLDA), preblooming and postblooming period.
3
25
Introduction
26
The intensification of olive production methods in southern Spain involves a widespread
27
use of chemicals and the progressive loss of many Mediterranean forest patches have led to
28
an impoverished arthropod fauna in olive agroecosystems (Ruano et al., 2004; Santos et al.,
29
2007). In Andalusian landscapes, natural and semi–natural vegetation have been removed
30
to increase the olive growing area that has led to a decrease and a fragmentation of the
31
original landscape (de Graaff & Eppink, 1999; Milgroom et al., 2007; Parra López &
32
Calatrava Requena, 2006). As a consequence of the destruction of the original landscape,
33
governments have developed legislation to regulate and support olive orchards cultivated
34
under more environmentally sound farming practices. This has created a need for reliable
35
monitoring of these substances in soil, plants and eatable products. Conventional analytical
36
techniques such as gas chromatography and mass spectrophotometry, are presently widely
37
used due to their accuracy, in spite of the costs involved (Denninson & Turner, 1995).
38
The Common Agricultural Policy reform of the European Union recently introduced
39
several new concepts and management activities for environmental protection taking into
40
account landscapes and environmental care (Yli–Viikaria et al., 2007). Agri–environmental
41
indicators (AEIs) are one of the tools intended to create a model that is easily understood
42
about the current state of agroecosystems. The richness and abundance of invertebrates
43
have been often used to distinguish farming systems for different crops (Álvarez et al.,
44
2000; Clough et al., 2007; Döring et al., 2003; Hadjicharalampous et al., 2002; Jackson et
45
al., 2007; Letourneau & Goldstein, 2001; Purtauf et al., 2005). An alternative approach is
46
to use a higher taxonomic level of insects and this is particularly useful when rapid
47
biodiversity surveys are required (Andersen, 1995; Oliver & Beattie, 1996), it may lighten
48
the workload for non–taxonomists, who require a rapid and cheap methodology of
4
49
certification for the organic olive farming system. Even though this is a recent approach
50
(Balmford et al., 1996; Williams & Gaston, 1994), other studies have been accepted for the
51
first phase of investigation as a shortcut to compare the biodiversity levels of the
52
agricultural landscapes (Biaggini et al., 2007).
53
Through the use of meta–analysis, Bengtsson et al. (2005) found that studies provided
54
evidence that organic farming usually enhances species richness, most notably of plants,
55
birds and predatory insects, however, they observed that these effects differ between
56
organism groups and landscapes. Furthermore, the landscape structure and heterogeneity
57
also affect the biodiversity in agroecosystems when a large geographical region is
58
considered (Benton et al., 2003; Burel et al., 1998; Dauber et al., 2003; Fahrig & Jonsen,
59
1998; Krebs et al., 1999; Marino & Landis, 1996; Weibull et al., 2000). Because the
60
validation of the use of indicators requires a large spatial and time scale to provide reliable
61
information (NERI, 1995), we completed a previous study that suggested the use of certain
62
insects groups, such as Coleoptera and Lepidoptera, as indicators of the olive farming
63
systems in the Granada province (Ruano et al., 2004). The novel aspects of the current
64
study are the consideration of internal variation in the Granada province and the inclusion
65
of a new area, the Cordoba province, with different traditions of land use intensity. The
66
evaluation of these landscapes was made using the concepts of either “hemerobiotic state”
67
or “hemeroby” (Jalas, 1955) that describe human impacts on ecosystems such as land–use
68
types, plant communities and soils (Sukopp, 1972; Blume and Sukopp, 1976; Sukopp,
69
1976; Bornkamm, 1980). As a measure for naturalness or, conversely, the human influence
70
on ecosystems, hemeroby can be used as a surrogate for land–use intensity and a
71
sustainability measure index for agricultural landscapes (Fu et al., 2006).
72
In particular, the following question should be addressed:
5
73
1. Do insect orders as higher taxonomic level indicate organic farming system in olive
74
orchards in southern Spain? We predicted that Coleoptera and Lepidoptera would attain
75
higher population densities in organic olive orchards in Granada province, and they could
76
be used to correctly discriminate the type of farming system.
77
2. Are the same insect orders suitable indicators of the organic farming system for the
78
Cordoba province? We hypothesized that landscape structure and heterogeneity might also
79
affect the insect assemblages in olive agroecosystems.
80
Materials and methods
81
Study zones
82
The study area covers regions of Cordoba and Granada provinces extending approximately
83
104 km from north to south and 117 km at its widest point from east to west with the
84
experimental fields located at an altitude of 400 m to 1100 m above sea level. They are two
85
of the largest commercial olive producing areas in southern Spain, but natural surroundings
86
and land use traditions make the olive landscape diverse, being the patches of natural
87
vegetation smaller in Granada than in Cordoba (Figure 1). Moreover, in Cordoba the
88
organic olive farming system is extensive, while the surrounding olive orchards in Granada
89
are cultivated under conventional and intensive farming systems.
90
Farming systems
91
Olive orchards from Granada and Cordoba with organic, integrated and conventional
92
farming systems were sampled in different years (Table 1). Based on previous observations
93
(Ruano et al., 2004), samplings were carried out in May (pre–blooming) and June (post–
94
blooming), since arthropod abundance presents the largest differences among farming
95
regimes.
6
96
The different farming systems were implemented in compliance with the legislation in
97
force at that time. In contrast to the conventional olive farming system, the integrated (order
98
of 23rd of July 1983 BOE of 05 August 1983, and order of 17th of November of 1989,
99
modified later by Real Decreto 1201/2002 BOE no. 287 of 30th November of 2002) and
100
organic systems (Council Regulation (EEC) no. 2092/91 of 24th June 1991) are based on
101
mechanisms of natural regulation, and they are also ecologically sound, economically
102
viable and appropriate for all organisms of the food–web. The farming practices of each
103
type are summarised in Table 1.
104
Collection of insects
105
The sampling unit was a block consisting of a row of five sampled trees separated by an
106
unsampled tree so that the distance between sampled trees was 20 m, and each block was
107
separated from the other blocks by two rows of unsampled trees. Six blocks per orchard
108
were sampled in 1999, five in 2000 and to four in 2003 in order to reduce the sampling
109
effort while maintaining a sufficient degree of accuracy. The total number of sampled
110
blocks was 105 over the three year period; it results from multiplying the number of
111
sampled blocks by the number of orchard sampled in each year. The canopies of the olive
112
trees were sampled by beating five times at four branches per tree (one branch per compass
113
orientation) that were chosen at random with an insect net that was 50 cm in diameter.
114
Samples from the canopies were frozen, and the insects were then separated from the
115
vegetal and non–organic remains. Adults and juveniles were identified to the taxonomic
116
level of order, and the total number of each taxon was recorded, but Euphyllura olivina
117
(Costa, 1839) (Hemiptera: Psyllidae) and the Heteroptera suborder were only separated
118
from the Hemiptera order. Thus, when we refer to the Hemiptera order, we refer only to the
119
Cicadomorpha, Fulgoromorpha and Sternorrhyncha (excluding E. olivina) suborders. When
7
120
referring to the Hymenoptera order, we exclude the Formicidae family because of the high
121
number of captured specimens.
122
Statistical analysis
123
Due to the non–normality of the data after several transformations, the different orders were
124
compared monthly among management regimes in each province and in both provinces by
125
the Kruskal–Wallis test. The lack of correlation between observations over time was
126
evaluated by applying the Durbin–Watson test.
127
A Non–parametric Linear Discriminant Analysis (NPLDA) model based on the k–Nearest
128
Neighbour (kNN) algorithm was applied because the data were not assumed to have a
129
multivariate normality, and this non–parametric method is based on the Mahalanobis
130
distance of each case to each of the groups’ centroids (Lachenbruch, 1975). The taxonomic
131
groups selected to perform the canonical functions were obtained using a stepwise variable
132
selection procedure (McGarigal et al., 2000; Muñoz Serrano, 1996). Two procedures for
133
validating canonical functions were carried out. The two discriminant functions (pre–
134
blooming and post–blooming) from Granada in the three years were performed and
135
validated using the leave–one–out (LOO) cross validation method because this procedure is
136
recommended when the sampling size is small. However, when the sample size is larger,
137
the split–sample validation method is recommended (McGarigal et al., 2000). This latter
138
method was applied to the data from the Cordoba province in 2003 and to the data from the
139
Granada province over the three years. Randomly selecting two groups from a full data set
140
(105 blocks), the first group was used to perform the function and the second was used to
141
validate it. These analyses were carried out with SPSS 17.0 for Windows.
142
Hemeroby index
143
A characterisation of the landscape based on the calculation of the Hemeroby index, M ,
8
144
was calculated (Steinhardt et al., 1999) for each olive grove at a buffered distance of 1000
145
m using the ArcGIS 9.3 software (Table 1). The characterisation describes gradients of
146
human influence on the landscape, and the data on hemeroby are given on a scale ranging
147
from level 1 (without actual human impact) to level 100 (artificial landscape elements that
148
do not resemble the originally prevalent biocoenoses). Furthermore, a characterisation of
149
the diversity based on the mean of the Shannon index at insect order level per orchard was
150
calculated monthly.
151
Results
152
Comparing farming systems
153
In 2003, 2780 specimens were captured in Granada province during the pre–blooming time,
154
and the percentages of each group represented were as follows: E. olivina (63.2%), Diptera
155
(8%), Hemiptera (7.6%), Lepidoptera (6%), the remaining 15% being Coleoptera,
156
Heteroptera, Dermaptera, Hymenoptera, Formicidae, Neuroptera, Orthoptera, Psocoptera,
157
Trichoptera, Thysanoptera and Zygentoma (Table 2). The highest abundance of insects was
158
observed in organic orchards followed by integrated orchards and finally, with the lowest
159
abundance, by conventional orchards. The greatest number of specimens, 8317 individuals,
160
were captured during the post–blooming period in Granada, and the percentages of each
161
order represented were as follows: E. olivina (72.5%), Heteroptera (8.1%), Hemiptera
162
(7.3%), Hymenoptera (3.6%) and the remaining 8.5% were Coleoptera, Diptera,
163
Thysanoptera, Dermaptera, Dictyoptera, Formicidae, Lepidoptera, Neuroptera, Orthoptera,
164
Psocoptera and Trichoptera (Table 2). The highest abundance of the E. olivina species was
165
found in the integrated orchards followed by the organic orchards and conventional
166
orchards.
167
In Cordoba canopies, 1068 specimens were captured during the pre–blooming period, and
9
168
the percentages of each order represented were as follows: E. olivina (29.5%), Hemiptera
169
(17.8%), Diptera (10.8%), Hymenoptera (9.2%), Coleoptera (8.2%) and the remaining
170
26.6% were Dermaptera, Dictyoptera, Formicidae, Heteroptera, Lepidoptera, Neuroptera,
171
Odonata, Orthoptera, Psocoptera and Thysanoptera (Table 2). The highest number of
172
specimens was captured in organic orchards, while insect abundance was lower but similar,
173
in both integrated and conventional ones.
174
A higher abundance of specimens was caught during the post-blooming than during pre-
175
blooming developmental stage. In the post-blooming stage, 1691 individuals were captured,
176
and the percentages of each order represented were as follows: E. olivina (37.2%),
177
Hemiptera (17.8%), Coleoptera (17.1%), Formicidae (7.7%), Heteroptera (7.3%) and the
178
final 12.5% represented by Diptera, Dermaptera, Dictyoptera, Hymenoptera, Neuroptera,
179
Odonata, Orthoptera, Psocoptera and Thysanoptera. The highest number of insects was
180
found in organic orchards, while the number of specimens was lower in the other two types
181
of orchards.
182
Discriminant function with interannual variation
183
The first approach in this study attempted to discriminate organic and non-organic orchards
184
from the Granada province. The three resampled orchards were shown to be independent
185
from each other over the three-year period by applying the Durbin-Watson test. The two
186
discriminant functions (pre-blooming and post-blooming) were performed using a dataset
187
from the Granada province in 1999, 2000 and 2003, and the cross validation of these
188
discriminant functions was performed using the LOO cross validation method. In the pre-
189
blooming period, the selected taxonomic groups included in the discriminant function were
190
the Hymenoptera, Lepidoptera and Hemiptera orders, and the rates of the well classified
191
non-organic and organic blocks were 95.7% and 56.5%, respectively (82.6% of the total
10
192
blocks). By contrast, the post-blooming period function had a correct classification of
193
97.8% for the non-organic blocks and 87% for the organic blocks (94.2% of the total
194
blocks) taking into account the following groups: Heteroptera, Lepidoptera, Hemiptera,
195
Formicidae and E. olivina. The LOO cross validation model correctly discriminated 92.8%
196
of the blocks (95.7% of non-organic and 87% of organic blocks). Even when the correct
197
classification rates of the LOO validation were slightly lower than the rate from the full
198
data set, a higher number of organic blocks were well discriminated in the post-blooming
199
group (Table 3).
200
Discriminant function with environmental heterogeneity
201
After applying the discriminant coefficients obtained from the Granada province to the
202
Cordoba blocks, no more than 25% of the Cordoba organic blocks could be well
203
discriminated in both time periods. After looking for a better approach, a second procedure
204
was applied. The split-sample validation method was used to validate the functions because
205
the sample size was large enough. The total number of blocks was randomly divided into
206
two groups. One group of the sampled blocks from each orchard was randomly selected,
207
and two subdata sets were created. First, the initial subset consisting of 53 blocks was used
208
to derive the discriminant functions. Then, the second dataset with 52 blocks was used to
209
validate the functions by the split-sample validation. On the one hand, the pre-blooming
210
function was performed using the hemipterans resulting in the correct classification of 70.4
211
% of the blocks (Table 3). The unstandardised coefficients obtained were used to classify
212
blocks from the second data set that resulted in a 52.9 % of correct classification rate for the
213
blocks, as compared to a 71.04% for the full data set. On the other one hand, the post-
214
blooming function used coleopterans (with the highest coefficient in the function) and
215
hemipterans as variables, and the function resulted in a correct classification of 77.8%
11
216
(Table 3). After validating the function with the second data set, 78.4% of the blocks were
217
correctly classified. Therefore, the resulting correct classification of the full data set was
218
78.1% and was similar to both the first and second data set (Table 3).
219
Correct classification rates for the pre-blooming and post-blooming periods were different.
220
In the pre-blooming period, a low number of organic blocks could be correctly classified
221
before and after the split-sample validation, whereas the resulting correct validation was
222
similar to or higher than the correct rate of the full data set in the post-blooming period
223
(Table 3). However, most of the organic blocks, especially from Granada, were
224
misclassified due to a low overall abundance of coleopterans.
225
Finally, to demonstrate the difficulties found in the discrimination between the Granada and
226
Cordoba orchards, a discriminant map containing three groups (non-organic and organic
227
blocks of Granada and all blocks of Cordoba) was plotted, showing that a clear spatial
228
distribution was achieved. The percentage of Cordoba blocks that were well discriminated
229
was 100% while 83.3% of organic and 75% of non-organic blocks from Granada were also
230
correctly classified (Figure 2).
231
According to the hypothesis that the landscape heterogeneity is affecting the diversity, a
232
Spearman's rank correlation coefficient was calculated to find the association between the
233
Hemeroby index and the Shannon index. A strong negative relationship (ρ = –0.54, n = 96;
234
p < 0.0001) was observed during the pre–blooming period, and a weaker correlation was
235
found in the post-blooming period (ρ = –0.21, n = 96; p < 0.05). These data suggested that
236
the highest diversity values can be expected in olive orchards, which are sourrounded by a
237
higher number of patches of natural or seminatural vegetation and more extensive farming
238
methods, as in the case of orchards from Cordoba.
239
12
240
Discussion
241
Comparing farming systems
242
As Ruano et al. (2004) also observed, organic olive orchards had the highest number of
243
captures followed by the integrated orchards in Granada and conventional orchards in
244
Cordoba, since the insects were not exposed to chemical treatments in organic orchards.
245
The Spearman correlation coefficients calculated indicate that biodiversity, at insect order
246
level, decreased in orchards where semi-natural habitats were present in low numbers. The
247
Cordoba region had a higher frequency of Mediterranean forest patches, which gave more
248
opportunities for the dispersal of species and functional groups of insects from relatively
249
undisturbed habitats into agricultural production areas (Altieri, 1999; Duelli & Obrist,
250
2003). Different farming practices have also been correlated with changes in the diversity
251
and species assemblage (Burel et al., 1998).
252
The abundance of the higher taxa varied in the same regions on a monthly and yearly basis,
253
which was already reported by Ruano et al. (2004), and it made it difficult to design a
254
linear discriminant function that would be useful and reliable anytime and anywhere else.
255
Discriminant functions
256
In this study, two models were studied that focused on the interannual and environmental
257
variability of the samples. The first model had high accuracy even when the same orchards
258
were evaluated over the time period. Some of the difficulties in classifying orchards over
259
the time period might have been due to the weather changes and to the changes in the
260
intensity of the agronomic practices from farmer to farmer and in the same orchard from
261
year to year.
262
Following the advice of Ruano et al. (2004), the sampling period was extended from May
263
to July in the regions. In our survey, it was confirmed that sampling in June (post13
264
blooming), as they recommended, was a reliable period to classify organic and non-organic
265
orchards. They also suggested that Coleoptera and Lepidoptera were bioindicators of the
266
organic olive orchards. In our study, however, Coleoptera and Hemiptera best discriminated
267
between organic and non-organic farming systems in the olive orchards in both provinces.
268
Coleopterans had the strongest contribution to the discriminant function, which could mean
269
that the higher beetle abundances are related to more sustainable practices. However, some
270
blocks that belonged to the organic orchards with low a abundance of captured beetles
271
could not be well classified, which could occur because an indicator does not work well for
272
all environments or at different spatial scales (Moreno et al., 2007). Regarding the
273
Lepidoptera order, no differences were found in Cordoba comparing management regimes.
274
Therefore, the use of lepidopterans as an indicator could not be validated. The insect
275
composition in the Cordoba orchards was quite different from the non-organic and organic
276
orchards of Granada. However, only the Coleoptera and Hemiptera orders were able to best
277
discriminate between the organic and non-organic farming systems in the olive orchards in
278
both provinces. The conventional, integrated and organic farming systems, in contrast,
279
could not be discriminated from each other based only on higher insect taxa.
280
In conclusion, it is recommended to sample in the post-blooming period and to use the
281
number of Coleoptera and Hemiptera (excluding Heteroptera and E. olivina) for
282
bioindicators. Even though there were differences in the insect composition between the
283
regions, the results suggest that high abundances of these two groups are potential
284
bioindicators to detect, in a simple way, more sustainable practice in the post-blooming
285
period.
286
References
287
Andersen, A. N. (1995). Measuring more of biodiversity: genus richness as a surrogate for
14
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
species richness in Australian ant faunas. Biological Conservation, 73, 39–43.
Altieri, M. A. (1999). The ecological role of biodiversity in agroecosystems. Agriculture,
Ecosystems & Environment, 74, 19–31.
Álvarez, T., Frampton, G. K. & Goulson, D. (2000). Epigeic Collembola in winter wheat
under organic, integrated and conventional farm management regimes. Agriculture,
Ecosystems & Environment, 83, 95–110.
Balmford, A., Green, M. J. B. & Murray, M. G. (1996). Using higher taxa richness as a
surrogate for species richness. I. Regional tests, Vol. 263, Proceedings of the Royal
Society. London, pp. 1267–1274.
Bengtsson, J., Ahnström, A. C. & Weibull, J. (2005). The effects of organic agriculture on
biodiversity and abundance: a meta–analysis. Journal of Applied Ecology, 42, 261–
269.
Benton, T. G., Vickery, J. A. & Wilson, J. D. (2003). Farmland biodiversity: is habitat
heterogeneity the key? Trends in Ecology & Evolution, 18, 182–188.
Biaggini, M., Consorti, R., Dapporto, L., Dellacasa, M., Paggetti, E. & Corti, C. (2007).
The taxonomic level order as a possible tool for rapid assessment of Arthropod
diversity in agricultural landscapes Agriculture, Ecosystems & Environment, 122,
183–191
Burel, F., Baudry, J., Butet, A., Clergeau, P., Delettre, Y., Le Couer, D., Dubs, F., Morvan,
N., Paillat, G., Petit, S., Thenail, C., Brunel, E. & Lefeuvre, J. C. (1998).
Comparative biodiversity along a gradient of agricultural landscapes. Acta
Oecologica, 19, 47–60.
Clough, Y., Kruess, A. & Tscharntke, T. (2007). Organic versus conventional arable
farming systems: Functional grouping helps understand staphylinid response.
Agriculture, Ecosystems & Environment, 118, 285–290.
Dauber, J., Mirsch, M., Simmering, D., Waldhardt, R., Otte, A. & Wolters, V. (2003).
Landscape structure as an indicator of biodiversity: matrix effects on species
richness. Agriculture, Ecosystems & Environment, 98, 321–329.
de Graaff, J. & Eppink, L. A. A. J. (1999). Olive oil production and soil conservation in
southern Spain, in relation to EU subsidy policies. Land Use Policy, 16, 259–267.
Denninson, M. J. & Turner, P. F. (1995). Biosensors for environmental monitoring.
Biotechnology Advances, 13, 1–12.
Döring, T. F., Möller, A., Wehke, S., Schulte, G. & Broll, G. (2003). Biotic indicators of
carabid species richness on organically and conventionally managed arable fields.
Agriculture, Ecosystems & Environment, 98, 133–139.
Duelli, P. & Obrist, M. K. (2003). Regional biodiversity in an agricultural landscape: the
contribution of semi–natural habitat islands. Basic and Applied Ecology, 4, 129–
138.
Fahrig, L. & Jonsen, I. (1998). Effect of habitat patch characteristics on abundance and
diversity of insects in an agricultural landscape. Ecosystems, 1, 197–205.
Hadjicharalampous, E., Kalburtji, K. L. & Mamolos, A. P. (2002). Soil Arthropods
(Coleoptera, Isopoda) in Organic and Conventional Agroecosystems.
Environmental Management, 29, 683–690.
Jackson, L. E., Pascual, U. & Hodgkin, T. (2007). Utilizing and conserving
agrobiodiversity in agricultural landscapes. Agriculture, Ecosystems &
Environment, 121, 196–210.
Junta_de_Andalucía. (2001). REDIAM: Red Medioambiental de Andalucía, Junta de
15
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
Andalucía. Sevilla.
Krebs, J. R., Wilson, J. D., Bradbury, R. B. & Siriwardena, G. M. (1999 ). The second
silent spring? Nature, 400, 611–612.
Lachenbruch, P. A. (1975). Discriminant analysis. Hafner, New York, 128 pp.
Letourneau, D. K. & Goldstein, B. (2001). Pest damage and arthropod community structure
in organic vs. conventional tomato production in California. Journal of Applied
Ecology, 38, 557–570.
Marino, P. C. & Landis, D. A. (1996). Effect of landscape structure on parasitoid diversity
and parasitism in agroecosystems. Ecological Applications, 6, 276–284.
McGarigal, K., Cushman, S. & Stafford, S. (2000). Multivariate Statistics for Wildlife and
Ecology Research. Springer–Verlag, New York, 283 pp.
Milgroom, J., Soriano, M. A., Garrido, J. M., Gómez, J. A. & Fereres, E. (2007). The
influence of a shift from conventional to organic olive farming on soil management
and erosion risk in southern Spain. Renewable Agriculture and Food Systems, 22,
1–10.
Moreno, C. E., Sanchez–Rojas, G., Pineda, E. & Escobar, F. (2007). Shortcuts for
biodiversity evaluation: a review of terminology and recommendations for the use
of target groups, bioindicators and surrogates. International Journal of Environment
and Health, 1, 71–86.
Muñoz Serrano, A. (1996). Estadística aplicada uni y multivariante (Tomos I e II). Junta de
Andalucía, Consejería de Agricultura y Pesca, 390 pp.
NERI. (1995). Nature indicators survey. Report to Topic Centre Paris. Ministry of the
Environment and Energy & National Environmental Research Institute, Denmark.
Oliver, I. & Beattie, A. J. (1996). Designing a cost-effective invertebrate survey: a test of
methods for rapid assessment of biodiversity. Ecological Applications, 6, 594–607.
Parra López, C. & Calatrava Requena, J. (2006). Comparison of farming techniques
actually implemented and their rationality in organic and conventional olive groves
in Andalusia, Spain. Biological Agriculture & Horticulture, 24, 35–59.
Purtauf, T., Roschewitz, I., Dauber, J., Thies, C., Tscharntke, T. & Wolters, V. (2005).
Landscape context of organic and conventional farms: influences on carabid beetle
diversity. Agriculture, Ecosystems & Environment, 108, 165–174.
Ruano, F., Lozano, C., García, P., Peña, A., Tinaut, A., Pascual, F. & Campos, M. (2004).
Use of artrhopods for the evaluation of the olive–orchard management regimes.
Agriculture and Forest Entomology, 6, 111–114.
Santos, S. A. P., Pereira, J. A., Torres, L. M. & Nogueira, A. J. A. (2007). Evaluation of the
effects, on canopy arthropods, of two agricultural management systems to control
pests in olive groves from north–east of Portugal. Chemosphere, 67, 131–139.
Steinhardt, U., Herzog, F., Lausch, A., Müller, E. & Lehmann, S. (1999). Hemeroby index
for landscape monitoring and evaluation. In Enviromental Indices – System Analysis
Approach, eds. Y. A. Pykh, D. E. Hyatt & R. J. Lenz, EOLSS Publ. Oxford, pp.
237–254.
Weibull, A.–C., Bengtsson, J. & Nohlgren, E. (2000). Diversity of butterflies in the
agricultural landscape: the role of farming system and landscape heterogeneity.
Ecography, 23, 743–750.
Williams, P. H. & Gaston, K. J. (1994). Measuring more of biodiversity: can higher–taxon
richness predict wholesale species richness? Biological Conservation, 67, 211–217.
Yli–Viikaria, A., Hietala–Koivub, R., Huusela–Veistolaa, E., Hyvönena, T., Peräläa, P. &
16
382
383
384
385
Turtolaa, E. (2007). Evaluating agri–environmental indicators (AEIs)—Use and
limitations of international indicators at national level. Ecological Indicators, 7,
150–163.
17
Table 1 Types of farming systems, year of sampling, agricultural practices used and hemeroby index, for the olive orchards studied in the
provinces of Cordoba and Granada.
Province
Cordoba
Granada
Olive
orchard
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
Farming
system
Sampling year
Conventional
2003
Integrated
2003
Organic
2003
Conventional
2003
Organic
2003
Conventional
2003
Integrated
2003
Organic
2003
Integrated
2003
Conventional 1999, 2000, 2003
Integrated
2003
1999 2000
Integrated
2003
Organic
2003
Integrated
2003
Conventional
2003
Organic
1999, 2000, 2003
Conventional
2003
Organic
2003
Vegetation
Irrigation Plough Insecticides Herbicides
Cover
No
No
No
No
Yes
No
No
No
No
Yes
Yes
No
No
Yes
Yes
No
Yes
No
Yes
No
Yes
No
Yes
Yes
No
Yes
No
Yes
Yes
No
Yes
Yes
No
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
No
Yes
No
Yes
Yes
No
Yes
No
Yes
Yes
No
Yes
No
Yes
No
No
Yes
Yes
Yes
Yes
No
No
No
Yes
No
Yes
No
Hedge Hemeroby
index
row
No
Yes
Yes
Yes
Yes
No
Yes
Yes
No
No
Yes
No
No
No
Yes
No
Yes
No
Yes
No
No
Yes
500 m
37
34.1
34.3
35.1
59.2
56.2
54.5
55.7
61.2
67.4
74.3
No
No
71
Yes
No
No
Yes
Yes
Yes
No
No
No
Yes
No
Yes
66.6
66.9
66.2
61.2
61.5
61.1
18
Table 2 Mean and standard deviation (SD) of each insect group captured per block in conventional (C), integrated (I) and organic (O) orchards
from Granada and Cordoba provinces in pre– and the post– blooming period 2003. Significant values per management in each province and both
provinces are indicated.
GRANADA
E. olivina
HEMIPTERA
(excluding E. olivina
and Heteroptera)
CORDOBA
KW–test
C
I
O
C
(gl=2)
Mean ± SD
Mean ± SD
χ2
p
Mean ± SD
Blooming Mean ± SD
16.83
±
13.22
53.00
±
62.82
76.67
±
70.73
9.4
**
3.25 ± 5.55
Pre–
64.17 ± 52.00 290.08 ± 228.61 148.50 ± 68.61
NS
12.08 ± 12.70
Post–
1.92 ± 3.99
7.67 ± 10.29
8.00 ± 11.24
NS
2.75 ± 2.99
Pre–
Post–
Pre–
Heteroptera
Post–
Pre–
HYMENOPTERA
(excluding Formicidae) Post–
Pre–
COLEOPTERA
Post–
Pre–
LEPIDOPTERA
Post–
Pre–
NEUROPTERA
Post–
Pre–
Formicidae
Post–
Pre–
DIPTERA
Post–
Pre–
PSOCOPTERA
Post–
THYSANOPTERA Pre–
3.92 ± 4.68
29.42 ± 27.31
17.33 ± 17.13
NS
1.08 ± 1.56
12.42 ± 18.96
1.92 ± 1.83
5.92 ± 5.37
1.50 ± 1.51
0.42 ± 0.67
2.50 ± 3.32
6.33 ± 7.18
1.67 ± 1.56
3.83 ± 2.59
0.17 ± 0.39
0.67 ± 0.89
5.25 ± 4.07
0.83 ± 0.83
0.08 ± 0.29
0.42 ± 0.67
1.00 ± 1.04
1.75 ± 2.53
38.00 ± 25.70
1.75 ± 1.22
5.83 ± 5.42
0.83 ± 1.27
1.17 ± 1.03
1.25 ± 1.48
1.92 ± 2.19
0.92 ± 1.16
2.50 ± 1.93
3.42 ± 4.14
5.50 ± 5.52
2.25 ± 2.45
1.17 ± 0.94
0.25 ± 0.62
0.25 ± 0.62
0.92 ± 1.51
6.50 ± 4.36 14.8 ***
5.67 ± 4.50
NS
2.42 ± 1.56
NS
13.08 ± 15.29 7.4 *
1.08 ± 0.67
NS
2.83 ± 3.51
6.4 *
10.25 ± 14.35
NS
15.58 ± 12.72 8.4 *
0.92 ± 1.38
NS
7.17 ± 3.90
NS
5.58 ± 13.12 9.5 **
1.08 ± 1.78
NS
11.08 ± 9.93 11.6 **
2.92 ± 2.47
NS
0.08 ± 0.29
NS
1.67 ± 1.92
NS
0.50 ± 0.90
NS
I
Mean ± SD
1.00 ± 1.71
3.67 ± 2.84
1.50 ± 1.17
KW–test
KW–test
(gl=2)
O
(gl=2)
Mean ± SD
χ2
p
χ2
p
19.33 ± 12.32 17.0 *** 13.3 *
36.67 ± 13.96 21.8 ***
NS
12.42 ± 6.86 18.5 *** 15.7 ***
9.17 ± 6.83
3.00 ± 2.83 12.92 ± 11.90 11.5 ***
0.50 ± 0.52
4.33 ± 7.25
0.92 ± 1.44
2.83 ± 1.75
1.50 ± 1.31
9.42 ± 9.70
0.67 ± 0.89
0.17 ± 0.39
0.83 ± 1.03
1.75 ± 2.34
1.00 ± 1.60
4.42 ± 7.76
1.92± 1.62
0.50 ± 0.67
0.50 ± 1.17
0.58 ± 0.90
1.17 ± 1.19
0.83 ± 1.34
3.25 ± 6.22
1.75 ± 1.48
1.67 ± 2.27
1.00 ± 0.95
5.42 ± 5.58
0.42 ± 0.51
0
0.83 ± 0.83
2.75 ± 2.80
2.00 ± 2.49
2.58 ± 2.75
2.58± 2.27
0.17 ± 0.39
0.17 ± 0.39
0.58 ± 1.08
0.92 ± 1.24
3.83 ± 1.85
2.75 ± 4.45
5.50 ± 3.53
2.00 ± 1.54
4.83 ± 3.30
9.25 ± 6.61
0.58 ± 0.67
0.25 ± 0.62
3.17 ± 2.52
2.42 ± 2.11
2.33 ± 2.15
3.83 ± 2.66
5.08± 5.11
0.25 ± 0.62
0.83 ± 1.34
0.92 ± 2.11
1.33 ± 0.89
NS
11.2 ** 18.5 ***
NS
NS
9.3 * 6.3
*
NS
NS
13.2 *
NS
NS
NS
NS
NS
NS
NS
8.5 *
NS
NS
NS
10.8 ** 17.0 ***
NS
6.5
*
NS
NS
NS
NS
NS
NS
NS
NS
8.9 * 8.5
*
19
ORTHOPTERA
DERMAPTERA
DICTYOPTERA
TRICHOPTERA
ODONATA
ZYGENTOMA
TOTAL
Post–
Pre–
Post–
Pre–
Post–
Pre–
Post–
Pre–
Post–
Pre–
Post–
Pre–
Post–
Pre–
Post–
0.33 ± 0.65
0
0.25 ± 0.62
0.08 ± 0.29
0
0
0
0
0
0
0
0.08 ± 0.29
0
409
1194
0.42 ± 0.90
0.08 ± 0.29
0
0.08 ± 0.29
0.17 ± 0.39
0
0.25 ± 0.45
0.25 ± 0.45
0.17 ± 0.58
0
0
0
0
893
4522
0.67 ± 0.98
0.08 ± 0.29
0.17 ± 0.58
0
0.08 ± 0.29
0
0
0
0
0
0
0
0
1478
2601
NS
NS
6.4 *
NS
NS
NS
NS
6.4 *
NS
NS
NS
NS
0.08 ± 0.29
0.08 ± 0.29
0.25 ± 0.45
0.08 ± 0.29
0
0
0
0
0
0
0
0
0
182
547
0.08 ± 0.29
0
0.17 ± 0.58
0.42 ± 0.67
0.25 ± 0.62
0
0
0
0
0
0
0
0
161
283
0.08 ± 0.29
0
0.25 ± 0.87
0.08 ± 0.29
0
1.00 ± 1.41
0.08 ± 0.29
0
0
0.08 ± 0.29
0.08 ± 0.29
0
0
725
861
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
P values are: *, < 0.05; **, < 0.01; ***,<0.005; NS, not significant (> 0.05)
20
Table 3 Unstandardised coefficients of the canonical discriminant function and the percentage of correct classification of A) LOO cross
validation method and B) split–sample validation method for each farming system and time period.
A) LOO cross validation method
Unstandardised
Coefficients
Preblooming
Postblooming
Hymenoptera*
Lepidoptera
Hemiptera**
Constant
Heteroptera
Lepidoptera
Hemiptera**
0.188
0.108
0.011
–1.150
0.021
0.081
0.056
Formicidae
0.236
B) Split–sample validation method
Non–organic
First data
set
%
95.7
Organic
56.5
56.5
Non–organic
97.8
95.7
Farming system
LOOCV
%
95.7
Organic
87.0
87.0
E. olivina
–0.006
Constant
–1.673
Insect group represents: *, excluding Formicidae; **, excluding E. olivina and Heteroptera
Unstandardised
Coefficients
Farming
system
First data
set
%
Resampling
data set
%
Full
dataset
%
Hemiptera**
0.050
Non–organic
86.1
55.9
85.7
Constant
–0.608
Organic
38.9
47.1
42.8
Coleoptera
0.170
Non–organic
88.9
85.3
87.1
Hemiptera**
–0.017
Constant
–0.474
Organic
55.6
64.7
60
21
Figure 1
22
Figure 2
23
Figure legends
Figure 1 Location of the organic, integrated and conventional orchards, with
surrounding land uses, in the provinces of Granada and Cordoba.
Figure 2 Discriminant distribution of non–organic and organic blocks from Granada
and all blocks from Cordoba.
24
Aknowledgements
We would like to thank the anonymous reviewers for their valuable comments to this
paper. The English version of this manuscript has been revised by the staff of American
Journal Experts and Mrs Mar Cotes–Ramal. This work was supported by the research
project AMB98–0946, REN2002–03269 and AGL2005–00932 funded by CICYT from
the Education and Research Spanish Ministry through Projects.
25