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# Crop heterogeneity may not enhance biological control of rice pests in landscapes rich in semi-natural habitats

Quanfeng Yang, Coline C Jaworski, Zhi Wen, Nicolas Desneux, Fang Ouyang,

Xuhuan Dai, Lijuan Wang, Jiao Jia, Hua Zheng

► To cite this version:

Quanfeng Yang, Coline C Jaworski, Zhi Wen, Nicolas Desneux, Fang Ouyang, et al.. Crop heterogeneity may not enhance biological control of rice pests in landscapes rich in semi-natural habitats. Agriculture, Ecosystems & Environment, 2025, 379, pp.109354. 10.1016/j.agee.2024.109354. hal-04879933

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Submitted on 10 Jan 2025

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22 **Abstract**

Agricultural landscapes are undergoing profound changes worldwide, including crop homogenization and loss of semi-natural habitats, which may be detrimental to arthropod natural enemies of crop pests, although interactive effects between these drivers remain understudied. We assessed the relative contribution of crop diversity, mean field size and the proportion of semi-natural habitats on the abundance and diversity of rice pests and their arthropod natural enemies (predators versus parasitoids) across four spatial scales (100, 250, 500 and 1000 m) in 17 rice fields in Hainan Island, China. The overall low abundance of pests and natural enemies measured in rice fields could indicate high biological control level. Semi-natural habitats (including some recently restored ones) were largely represented in the studied landscapes (average proportion of 65%) and may be biodiversity reservoirs. Indeed, decreased mean field size and increased semi-natural habitat proportion promoted rice pests at the largest spatial scale only, possibly by enhancing their mobility and use of alternative resources. Predators' response was weak and indicated a positive influence of semi-natural habitats (or crop diversity in absence of semi-natural habitats) at small spatial scales. Parasitoid abundance was low, but tended to support the resource concentration hypothesis (reduced by crop diversity and increased by mean field size). Overall, crop heterogeneity favoured pests, but not natural enemies, its two components reduced mean field size and increased crop diversity were somewhat complementary and modulated by the proportion of semi-natural habitats but depended on spatial scale.

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41 **Keywords:** agroecosystem; biocontrol; crop diversity; landscape complexity; landscape composition
42 and configuration; mean field size; pest management.

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44
45 **1 Introduction**

46 Agricultural intensification is a major cause of biodiversity loss worldwide due to landscape
47 simplification and heavy reliance of inputs such as pesticides (Tscharntke et al. 2005; van Klink et al.
48 2020; Wagner 2020). Insecticides are massively used because of pest problems causing heavy yield
49 losses, despite their negative impacts on the environment, non-target species including pests’ natural
50 enemies, and human health (Stehle and Schulz, 2021). In addition, agricultural landscapes worldwide
51 have been undergoing profound changes, including reduced crop heterogeneity through a decreased
52 diversity in arable crops and increased mean field size, but also a decrease in semi-natural habitat areas
53 (Tscharntke et al. 2005, 2021; Clough et al. 2020). These changes have caused significant declines in
54 the abundance and diversity of natural enemies of crop pests, weakening biological pest control services
55 (Rusch et al. 2016; Seibold et al. 2019).
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57 To promote the conservation of natural enemies and biological pest control services, landscape
58 complexity can be restored by (i) restoring semi-natural habitats (Tscharntke et al. 2005; Gurr et al. 2017;
59 Yang et al. 2021, 2023); and (ii) increasing the heterogeneity of the crop mosaic itself through increased
60 crop diversity and reduced mean field size (Sirami et al. 2019). While the importance of semi-natural
61 habitats for natural enemy conservation has been relatively well studied, but comes at a cost of reducing
62 agricultural land, the effect of manipulating crop heterogeneity across spatial scales to enhance natural
63 enemies and biological pest control remains less understood (Jaworski et al. 2023). In particular, the
64 interactive effects of increased crop heterogeneity and proportion of semi-natural habitats are difficult
65 to predict and often non-linear (Jaworski et al. 2023).
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67 Landscape heterogeneity has two key components: landscape composition, i.e., the diversity of
68 different crop types and non-crop patches, and landscape configuration, i.e., the shape and spatial
69 arrangement of these different patches (Fahrig et al. 2011). These two components influence pest
70 suppression through trophic interactions between pests and their natural enemies, predators and
71 parasitoids. Increasing crop diversity can provide multiple habitat and food resources and increase the
72 temporal continuity of these resources to generalist predators including ladybugs (aphid specialists also
using floral resources), predatory bugs (feeding on aphids, planthoppers, floral resources, etc.), spiders (generalist prey hunters; Ratnadass et al. 2012; Jaworski et al. 2022). Conversely, crop diversity may be detrimental to parasitoids, which are specialists (attacking a restricted range of host species), and therefore tend to concentrate where host resources are important (i.e., in large monocultures; resource concentration hypothesis; Pareja et al. 2008; Gagic et al. 2012). Increased crop diversity may also cause locally higher pest densities, i.e. the crowding of host crop patches (Schneider et al. 2015). Therefore, increasing crop diversity may not necessarily result in improved biological pest control (Tscharntke et al. 2016; Jaworski et al. 2022).

Insect pests and natural enemies often move back and forth between crop fields and surrounding semi-natural habitats (Schellhorn et al. 2014). Therefore, the proportion of semi-natural habitats in a landscape can significantly affect the abundance of crop pests and of natural enemies (Schneider et al. 2016). Semi-natural habitats such as flower strips, forests and grasslands provide alternative food resources and shelter and create microclimate for natural enemies, which can promote pest control (González et al. 2015; Gurr et al. 2017; Yang et al. 2023). However, such semi-natural habitats may also be used by crop pests as alternative feeding, reproduction, and overwintering sites, promoting pest occurrence (Laterza et al. 2023; Thomine et al. 2023). Landscape configurational heterogeneity (e.g., decreased mean field size) may also benefit natural enemies by increasing their capacity to colonize crop fields from semi-natural habitats, or by improving access to resource spatio-temporal continuity over shorter spatial scales (Schneider et al. 2016; Sirami et al. 2019; Jaworski et al. 2022). Therefore, benefits of increased crop diversity and semi-natural habitat proportion interactively affect natural enemy abundance, and crop diversity tends to promote natural enemies in landscapes with intermediate proportions of semi-natural habitats (between 20 and 50%; Jaworski et al. 2023).

Effects of landscape composition and configuration on pest control vary across spatial scales, due to the dispersal capacity and ecology of crop pests and their natural enemies (Chaplin-Kramer et al. 2011; Haan et al. 2020). For instance, increased mean field size was often found to correlate to increased pest problems (Larsen and Noack, 2017; Tscharntke et al. 2021), but not at very small spatial scales (e.g.,
from 0.1 to <0.5 ha; Jaworski et al. 2022; Rosenheim et al. 2022). In a multi-scale analysis, densities of generalist arthropod predators increased with increased crop diversity over small spatial scales (100-250 m radius, i.e., 3-20 ha), while densities of aphid specialist parasitoids increased with increased crop diversity over very large spatial scales (2,000-3,000 m radius, i.e., 1,000-3,000 ha; Redlich et al. 2018).
In another study, ladybird abundances in cotton fields were not increased with increasing crop diversity at very small spatial scale (from 0.025 to 2 ha; Jaworski et al. 2022). Conversely, Thomine et al. (2023) found that lacewing abundance - but not ladybug abundance - increased with increasing edge density (a measure of landscape configuration), but not with increasing crop diversity in cotton fields at an intermediate spatial scale (500 m radius, i.e., 80 ha). The effects of semi-natural habitat on natural enemies also depend on the spatial scale considered (Jaworski et al. 2023). Therefore, understanding the interactive effects of crop diversity, mean field size, and semi-natural habitat proportion on natural enemy functional groups across spatial scales is key to restore biological pest control services.

Rice is one of the three main staple food crops (maize, wheat) worldwide, but more than 20% of rice yield is lost due to insect pests (Huang et al. 2014). The main insect rice pests are the specialist planthoppers (*Sogatella furcifera*; Hemiptera: Delphacidae) and generalist leafhoppers (*Nephottetix* spp.; Hemiptera: Cicadellidae), and the generalist true bugs (Hemiptera) and locusts (Orthoptera). Their natural enemies include generalist predators such as spiders and predatory bugs, which have a strong dispersal capacity, as well as specialist parasitoids, which tend to have a smaller dispersal capacity (Zhu et al. 2023). Rice agroecosystems are undergoing profound changes with reduced crop heterogeneity and the reduction of semi-natural habitat areas (GRiSP, 2013; Zou et al. 2020; Gong et al., 2024). Restoring semi-natural habitats around rice fields has been shown to conserve natural enemies and increased pest suppression (Ali et al. 2019; Gurr et al. 2016). However, to our knowledge, the interactive effects of semi-natural habitats and crop heterogeneity on pest-natural enemy interactions in rice cropping systems have not been investigated across spatial scales. This limits the potential to restore rice agrosystems for conservation biological control.

We investigated the interactive effects of crop heterogeneity (crop diversity and mean field size)
and semi-natural habitat proportion on biological pest control across four spatial scales (100, 250, 500, 1,000 m radius circles) in 17 rice fields in the northeast part of Hainan Island, a biodiversity hotspot (Myers et al. 2000). We measured the abundance and diversity of arthropod rice pests and natural enemies. We asked: i) Do increased crop diversity and reduced mean field size interactively enhance pest suppression? ii) Is such effect of crop heterogeneity regulated by the proportion of semi-natural habitats? iii) Do these effects depend on the spatial scale considered?

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## 2 Materials and methods

### 2.1 Study area and experimental design

The study took place in 2022 in a cultivated region of 80 × 40 km in Wenchang City, Hainan Island, southeast China (Fig. S1). This region has a tropical monsoon climate. The average temperature was 23.83 °C and the total precipitation was 898.70 mm at Wenchang City during the early rice stage (January to May) in 2022. A diversity of arable crops is cultivated in Hainan Island, including rice, soybean and especially high-input industrial crops such as lychee, areca and mango. 17 rice fields were selected (Fig. S1) along orthogonal gradients of crop diversity and mean field size as much as possible across four spatial scales (100 m, 250 m, 500 m and 1000 m; Figs. S1-S5), and also encompassing a gradient of semi-natural habitat proportion. Rice field size ranged from 0.04 to 0.11 ha with an average of 0.083 ha (Table S1). Rice fields were managed using standard commercial practices, with no pesticide application during the whole growing season.

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### 2.2 Crop heterogeneity and semi-natural habitat variables

We used high-resolution (0.8 m) remote sensing data of Wenchang City in 2021. We delineated all fields and attributed each field to one of the following 19 crop types, including rice, soybean, sweet potato, cassava, areca, coconut, jackfruit, litchi, orange, pomegranate, longan, dragon fruit, pineapple, grapefruit, jujube, pepper, chili, wax gourd, and mixed vegetables (Fig. 1, Fig. S6). We also delineated non-crop patches, including semi-natural habitats, urbanized habitats, and waterbody (Fig. S6).
diversity was calculated across four spatial scales (100, 250, 500, and 1000 m radii from the centre of each field) as Shannon Wiener Index; it ranged from 0.068 to 0.932 (Table S1). Mean field size was defined as the average size of all crop fields in each landscape at each spatial scale; it ranged from 0.2 to 6.0 ha (Table S1). Semi-natural habitat proportion ranged from 0 to 85% of the area across landscapes and spatial scales, with an average of 52% (Table S1); it was composed mainly of forest (57% in average), grassland (41%), and a small portion of shrubland (<1%; Table S2). China has already recently implemented extensive plans to return farmland to forests to restore semi-natural habitats, explaining these relatively high values (Tong et al. 2023); it is therefore essential to test the efficiency of such measures in promoting biological pest control in this area. The largest selected spatial scale of 1 km as well as smaller spatial scales of 100 and 250 m are commonly used to study the relationships between landscape structure and arthropod abundances in crop fields (Bosem et al. 2017; Sirami et al. 2019).

## 2.3 Sampling of arthropod pests and natural enemies

Arthropod communities in each field were sampled during the dry season (March to May 2022), in three rounds coinciding with the rice jointing, filling, and mature stages to reflect the occurrence and diversity of insect pests and natural enemies. In each rice field, three sampling points were placed equitably distributed along a transect covering field length (at roughly ¼, ½ and ¾ of the distance of the field's long side) and at least 5 m apart from the field border to minimize edge effects (Rand et al. 2006). They were marked with sticks for later resampling. At each sampling point, a nylon mesh enclosure (0.35 m × 0.35 m × 0.93 m) was placed above rice plants and arthropods were collected using a Vortis insect suction sampler (Burkard Manufacturing Company Limited) for 1 min (Zhou et al. 2016; Dominik et al. 2018). Rice herbivore and natural enemy arthropod specimens preserved in 75% ethanol were classified and identified to the species level (or morphospecies where specimens could not be adequately identified to the species level) using a binocular microscope and referring to the taxonomic books of He and Pang (1986) and Institute of Zoology, Chinese Academy of Sciences (1987). Natural enemies were divided into two groups: predators and parasitoids. Samples from the three rounds from each sampling point were pooled to obtain three values of total abundance and three values of Shannon Index for the
three functional groups of rice herbivores, predators and parasitoids for each field.

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## 2.4 Statistical analysis

All analyses were performed using R Core Team (2020) version 4.0.2. The responses of rice arthropods to crop heterogeneity variables - crop diversity (CD) and mean field size (MFS) - and semi-natural habitat proportion (SNH) were analysed using a linear mixed-effect model for each response variable at each spatial scale (R package 'lme4'; Bates et al. 2015). The response variables were the abundance or Shannon Index of each functional group and were log-transformed (natural constant *e*) prior to analyses to meet normality assumptions (Pinheiro et al. 2016). The fixed effects were CD, MFS, SNH and their interactions CD:MFS, CD:SNH, MFS:SNH, and CD:MFS:SNH. The random effect was the sampling field. The "check_collinearity" function from the 'performance' R package (Lüdecke et al. 2015) was used to check for multicollinearity among the three landscape predictors (CD, MFS, and SNH). Low correlation was found (all VIFs < 2.14, **Table S3**). Spatial autocorrelation among the three landscape predictors was also assessed for each spatial scale (**Figs. S2-S5**). When predictors were correlated at a given spatial scale, they were tested in alternative models, as detailed in **Table S4**. All models were verified for the absence of excessive discreteness (**Table S5**).

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An information-theoretic approach was used to quantify the strength of alternative competing models with ΔAICc < 7 (Burnham et al. 2011). Estimates with 95% confidence intervals (95% CIs) for each predictor were calculated through model averaging across the model sets., using the 'MuMln' R package (Barton 2013). Using the package 'glmm.hp' (Lai et al. 2022), we further calculated the contribution of each fixed effect and random effect to the variance in each alternative model, for each response variable and scale. Because this function requires at least two fixed effects implemented in the model to compute predictor relative contributions, CD was added as fixed effect in addition to SNH for the 1000 m spatial scale.

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## 3 Results

In total, we collected 1,292 arthropod specimens, for an average of 8.4 ± 0.7 [95%CI: 6.8, 9.7]
specimens per sampling point per date. Out of these, 699 were neither rice pests nor natural enemies. Among the remaining 593 individuals, 358 were rice herbivorous pests (60%), 147 were predators (25%), and 88 were parasitoids (15%). Rice herbivorous pests were dominated by green leafhoppers (Cicadellidae, 150 individuals); whitebacked planthoppers were the second most abundant (*S. furcifera*, 19 individuals). The most abundant predators were rove beetles (Staphylinidae, 41 individuals), dwarf spiders (Linyphiidae, 22 individuals), wolf spiders (Lycosidae, 17 individuals), long-jawed orb weavers (Tetragnathidae, 7 individuals), and the mirid bug *Cyrtorhinus lividipennis* (4 individuals). Finally, the most abundant families of parasitoids were Braconidae (19 individuals), Diapriidae (13 individuals) and Pteromalidae (16 individuals). The abundance and diversity of all rice arthropods (herbivores, predators, parasitoids and neutral insects) marginally significantly increased with the increase of semi-natural habitat proportion at large scales (500 m for abundance, 1000 m for diversity; **Fig. S8**).

The relative size effect (Figs. 2-3) and importance (Figs. S9-S14) of crop diversity, mean field size, semi-natural habitat proportion and their interactions varied across the four spatial scales for each functional group (herbivores, predators and parasitoids), and were overall quite similar between abundance and Shannon Index metrics. Overall, the best models (lowest AICc, Figs. 2-3, Table S6) coincided with the 1000 m scale, except for predator abundance (all scales equivalent), for parasitoid abundance (500 m yielded lowest AICc), and for parasitoid Shannon Index (all scales equivalent).

## 3.1 Herbivores

Increased mean field size had a negative effect on herbivore abundance at a 1000 m scale, but this was not true for smaller spatial scales (Fig. 2A-D, Table S6). Conversely, increased semi-natural habitat proportion had a marginally significant positive effect on herbivore abundance at a 1000 m scale, but this effect was lessened at a 500 m scale, and not different from zero at even smaller spatial scales. Mean field size explained 9% of variance in model 1, and semi-natural habitat proportion explained 8% of variance in model 2 at a 1000 m scale (Fig. S9). All other predictors had no significant effects on herbivore abundance, however the spatial scale considered.
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## 3.2 Predators

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## 3.3 Parasitoids

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Similar to herbivore abundance, decreased mean field size had a negative effect on herbivore Shannon Index, but only at a 1000 m scale (Fig. 3A-D, Table S6). It explained 11% of variance in model 1 (Fig. S11). All other predictors had no significant effects on herbivore abundance, however the spatial scale considered.

Predator abundance was marginally significantly influenced by the interaction between mean field size and semi-natural habitat proportion (MFS:SNH) at a 100 m scale, but not at larger spatial scales (Fig. 2E-H, Table S6). This was because increasing mean field size in landscapes with low SNH tended to yield higher values of predator abundance, but not in landscapes with higher SNH (Fig. 4A). Predator abundance was marginally significantly influenced by the interaction between crop diversity and mean field size (CD:MFS) at a 250 m scale, but not at other spatial scales. This was because crop diversity increased predator abundance in landscapes with smaller mean field size only (Fig. 6B). However, MFS:SNH explained only 2% of variance in model 2 at a 100 m scale, and CD:MFS explained only 2% of variance at a 250 m scale (Table S7; Fig. S10). All other predictors had no significant effects on predator abundance, however the spatial scale considered. Exactly the same trends were found for predator Shannon Diversity Index, except that MFS:SNH had a stronger, significant effect and explained 5% of variance in model 1 (Table S7; Fig. S13) at a 100 m scale, while CD:MFS explained 2% of variance at a 250 m scale (Figs. 3E-H, S12).

Parasitoid abundance was significantly influenced by the interaction between crop diversity and semi-natural habitat proportion (CD:SNH) at a 500 m spatial scale but not at other spatial scales (Figs. 2I-L, 4D). This was because parasitoid abundance decreased with increasing crop diversity only in landscapes with low SNH. In addition, crop diversity had a marginally significant negative effect on
parasitoid abundance at a 500 m scale. Both effects were only true at a 500 m scale, where CD:SNH and CD explained 6% and 9% of total variance, respectively (Fig. S11). Finally, at a 250 m scale, mean field size had a marginally significant positive effect on parasitoid abundance, where it explained 6% of variance, but this was not true at other spatial scales.

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Unlike predators, landscape predictors had a very different effect on parasitoid Shannon Diversity Index compared to parasitoid abundance (Fig. 3I-L). At a 250 m scale only, CD:SNH had a marginally significant effect on parasitoid diversity, explaining 1% of variance (Fig. S14). Specifically, parasitoid diversity increased with increased crop diversity in landscapes with low SNH only (Fig. 4F). At a 100 m scale only, MFS:SNH had a marginally significant effect on parasitoid diversity, explaining 3% of variance (Fig. 4E). This was because parasitoid diversity tended to decrease with increasing mean field size in landscapes with high, but not low SNH.

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## 4 Discussion

Our study explored whether increasing crop heterogeneity - by increasing crop diversity (CD) and reducing mean field size (MFS) - can enhance rice pest suppression depending on the semi-natural habitat proportion (SNH) and across spatial scales. Overall, the effects of these landscape predictors were weak and varied across spatial scales. Herbivores only responded at the largest spatial scale of 1,000 m: increased MFS significantly reduced their abundance and diversity, while increased SNH marginally increased their abundance. Conversely, predators responded at the two smallest spatial scales, with marginally significant negative effects of the interactions between CD and MFS at 250 m, and between MFS and SNH at 100 m; these effects were similar for predator abundance and diversity. Finally, parasitoids responded at intermediate spatial scales, where crop diversity reduced parasitoid abundance only in landscapes with low SNH at 500 m, while increased MFS tended to increase parasitoid abundance at 250 m. However, they responded differently in terms of diversity, with a positive effect of increased MFS at 100 m and a positive effect of crop diversity at 250 m in landscapes with low to intermediate SNH only. Our results show that it is fundamental to take into account the
293 spatial scale considered to restore landscapes promoting efficient ecosystem services.

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295 One key result is that the majority (54%) of arthropods caught in the suction samplings were neither
296 rice herbivorous pests nor natural enemies of rice pests, and this despite the validity of our sampling
297 method, calibrated on recommended durations for sampling efficiency (Zhou et al. 2016). Contrary to
298 similar studies on landscape-scale crop diversity effects (e.g., Redlich et al. 2018), rice was not a
299 massively dominant crop in this region (in average, 56% of the crop area, and 10% of the total area at a
300 1,000 m scale), while semi-natural habitat proportion was relatively high (in average 65% at a 1,000 m
301 scale), potentially explaining the low prevalence of rice pests and their natural enemies in rice field
302 catches in our study. It would be interesting, in future studies, to compare abundances of herbivores
303 (including crop pests) and their natural enemies in crop fields and in surrounding semi-natural habitats.
304 Also, and despite Hainan Island is embedded into a biodiversity hotspot (Myers et al. 2000; Liu et al.
305 2018), large portions of these semi-natural habitats in the study area have been restored only recently in
306 this part of the island (Tong et al. 2023); therefore, it is possible that they have not yet reached their full
307 potential in hosting arthropod biodiversity.

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309 We found that herbivorous pests only responded to landscape predictors at the largest spatial scale
310 (1 km), which was also found by Zhang et al. (2020). According to the resource concentration hypothesis,
311 pests should be more abundant in large monocultures (Root 1973). However, at large scale (1000 m),
312 we found that both increased crop configurational heterogeneity (reduced mean field size) and increased
313 semi-natural habitat proportion promoted rice herbivores, which were mostly polyphagous pests
314 (Figs. 2D, 3D). In our landscapes, smaller fields could have promoted rice pest dispersal across patches
315 with favourable resources (Sirami et al. 2019), such as rice crops but also grasses - abundant in semi-
316 natural habitats (Tables S1-S2) - used by leafhoppers and grasshoppers (McNeill et al. 2021). These
317 polyphagous pests have a strong dispersal capacity, which might explain why they responded to
318 landscape predictors at the largest spatial scale only.

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320 The ecology of natural enemies (such as feeding habits and mobility) led to important differences
in their responses to crop diversity, as previously reported (Jaworski et al. 2022). In general, generalist predators have strong dispersal capacity (Symondson et al. 2002). In our study, most predators were generalists, such as spiders and rove beetles, and they had a weak response to landscape predictors, at small spatial scales only. This could indicate that they use multiple habitats over short temporal scales, moving back and forth between different habitat patches - including SNH - to find alternative resources and/or shelter (Schellhorn et al. 2014; Gurr et al. 2017; Gallé et al. 2018). Specifically, we found that increased mean field size tended to reduce predator diversity and abundance in rice fields in landscapes with high SNH only, at a 100 m scale. This could be because they would not spillover far from the edge into large fields in landscapes with high SNH availability if such habitats can provide suitable resources (Woodcock et al. 2016). Conversely, increased crop diversity tended to increase predator abundance and diversity in landscapes with low SNH only, at a 250 m scale. This could be because crop diversity could partially compensate for reduced semi-natural habitat availability in promoting generalist predators by providing access to and spatio-temporal continuity of alternative resources (Jaworski et al. 2023). Rice pest incidence is generally low in the region, and farmers do not need to use large amounts of insecticides (authors' pers. obs.). This could further explain the low prevalence of natural enemies in rice fields if host / prey resources are low compared to adjacent semi-natural habitats. A reliance of predators on semi-natural habitats over short time scales would also explain why landscape effects were apparent at small spatial scales only.

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Parasitoids had a very distinct response from that of predators in our study. Parasitoid abundance responded to landscape predictors at intermediate spatial scales only. At 250 m, it was promoted by increased mean field size, which is in line with the resource concentration hypothesis, where rice specialist parasitoids could be attracted to large patches of host resources (Pareja et al. 2008; Gagic et al. 2012; Jaworski et al. 2022). At a 500 m spatial scale, parasitoid abundance was significantly increased by crop diversity in habitats with high SNH only, but was otherwise reduced by crop diversity. A negative effect of crop diversity on parasitoid abundance can also be explained by the resource concentration hypothesis, where increased crop diversity would dilute the concentration of host resources (Jaworski et al. 2022). However, a positive interactive effect of crop diversity and SNH may suggest that
parasitoids can become more abundant in very diverse landscapes, potentially through the use of alternative resources such as nectariferous flower resources (Gurr et al. 2017). Similar to predators, crop diversity effects may not be visible at a smaller spatial scale where crop diversity is too constrained by field size.

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Parasitoid diversity had a different response to landscape predictors than parasitoid abundance, and only at the smallest spatial scales. It tended to be reduced by increased mean field size at a 100 m scale, and by increased crop diversity at a 250 m scale, but only in landscapes with high SNH. While increased mean field size would reduce the diversity of host resources, and thereby the diversity of specialist parasitoids, increased crop diversity would be expected to have an opposite effect, especially for parasitoids of polyphagous pests. In addition, if semi-natural habitats provide host resources, they should also boost parasitoid diversity, and therefore parasitoid diversity would be expected to be higher, not lower, in landscapes with high SNH. Our unexpected results may be an artifact of the small parasitoid sampling sizes, and of the diversity metrics calculated based on identification to the family level.

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Increasing crop heterogeneity does not necessarily lead to improved pest control services, particularly in landscapes with high proportions of semi-natural habitats (reviewed in Jaworski et al., 2023): in our study, we found that it promoted pests (likely because they were polyphagous pests), but with no strong benefits for natural enemies. It would be interesting to replicate the study in landscapes widely dominated by rice crops, as well as in landscape with lower proportions of semi-natural habitat, and with more ancient semi-natural habitat, as these may all have significantly shaped the outcomes of our study. In the other hand, and likely best suited in regions with high crop diversity, it could be useful to consider agroecosystem restoration promoting biological pest control services across all crop types (not just on one focus crop) at the landscape level. The characteristics of the agroecosystems, including crop diversity and mean field size but also semi-natural habitat quantity, quality and connectivity all interact to shape arthropod abundances and diversity and ultimately ecosystem services. Although we still lack a fine understanding of - and testable ecological hypotheses on - the processes that determine the responses of specific arthropod functional groups to landscape predictors, it is necessary to take into
377 account the multiple dimensions of landscape complexity across spatial scales to inform landscape

378 management promoting efficient biological pest control services.

379

380

381 **Supplementary information** The online version contains supplementary material available at xxx.

382

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553

554

555 FIGURES

556
<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Example of a land cover map used to calculate landscape predictors within each landscape and each spatial scale (shown with concentric circles). Maps of the 17 study sites are shown in Fig. S6.</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Relative effect estimates of crop diversity (CD), mean field size (MFS), and semi-natural habitat proportion (SNH), as well as the second-order and third-order interaction terms (CD:MFS, MFS:SNH, CD:MFS:SNH) on the total abundance of (A-D) rice herbivores, (E-H) predators, and (I-L) parasitoids across four spatial scales: (A,E,I) 100 m, (B,F,J) 250 m, (C,G,K) 500 m, and (D,H,L) 1000 m. Predictor estimates (plain dots) and 95 % CI (horizontal arrows) and names are shown in three colours: black = crop heterogeneity (CD, MFS), gold = semi-natural habitat proportion (SNH), cyan = interactive effects between crop heterogeneity and SNH (CD: MFS, CD: SNH, CD: MFS: SNH). AIC values of models 1 and 2 (Table S4) are shown in each panel along with the total contribution of fixed effects (CD, MFS, SNH and their interactions) to the variance.</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Relative effect estimates of crop diversity (CD), mean field size (MFS), and semi-natural habitat proportion (SNH), as well as the second-order and third-order interaction terms (CD:MFS, MFS:SNH, CD:MFS:SNH) on the Shannon Diversity Index of (A-D) rice herbivores, (E-H) predators, and (I-L) parasitoids across four spatial scales: (A,E,I) 100 m, (B,F,J) 250 m, (C,G,K) 500 m, and (D,H,L) 1000 m. Predictor estimates (plain dots) and 95 % CI (horizontal arrows) and names are shown in three colours: black = crop heterogeneity (CD, MFS), gold = semi-natural habitat proportion (SNH), cyan = interactive effects between crop heterogeneity and SNH (CD: MFS, CD: SNH, CD: MFS: SNH). AIC values of models 1 and 2 (Table S4) are shown in each panel along with the total contribution of fixed effects (CD, MFS, SNH and their interactions) to the variance.</figcaption></figure>
<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /><figcaption>**Fig. 4.** Interactive effects of (A) MFS and SNH and (B) CD and MFS on predator abundance, (C) MFS and CD on predator Shannon Index, (D) CD and SNH on parasitoid abundance, and (E) MFS and SNH and (F) CD and SNH on parasitoid Shannon Index. Corresponding spatial scales are shown to the right of each panel. Only significant (p < 0.05; D) or marginally significant (p < 0.1; A-C,E-F) interactive effects are shown (Figs. 2–3). MFS: mean field size; SNH: semi-natural habitat proportion; CD: crop diversity.</figcaption></figure>
## Supporting Information for the article:

# Crop heterogeneity may not enhance biological control of rice pests in landscapes rich in semi-natural habitats

Quanfeng Yang, Coline C. Jaworski, Zhi Wen, Nicolas Desneux, Fang Ouyang, Xuhuan Dai, Lijuan Wang, Jiao Jia, & Hua Zheng*

## Table of Content

<table><tr><td>Supplementary tables</td><td>2</td></tr><tr><td>Table S1. Crop heterogeneity gradients in 17 rice landscapes</td><td>2</td></tr><tr><td>Table S2. The proportion of forest, grassland and shrubland in semi-natural habitats around each rice field across the four spatial scales</td><td>3</td></tr><tr><td>Table S3. Multicollinearity check between the three landscape predictors</td><td>4</td></tr><tr><td>Table S4. Tested models for each spatial scale</td><td>5</td></tr><tr><td>Table S5. Results of excessive discreteness tests at each spatial scale and for each response variable</td><td>6</td></tr><tr><td>Table S6. Estimates and model weights</td><td>7</td></tr><tr><td>Table S7. Model-averaged estimates with unconditional lower and upper 95% confidence intervals</td><td>20</td></tr><tr><td>Supplementary Figures</td><td>26</td></tr><tr><td>Fig. S1. (A) Research area (Hainan province) and (B) the location of the 17 sampled rice fields</td><td>26</td></tr><tr><td>Fig. S2. Correlation graphs between the second-order interaction terms CD:MFS, CD:SNH, and MFS:SNH at the 100 m spatial scale</td><td>26</td></tr><tr><td>Fig. S3. Correlation graphs between the second-order interaction terms CD:MFS, CD:SNH, and MFS:SNH at the 250 m spatial scale</td><td>27</td></tr><tr><td>Fig. S4. Correlation graphs between the second-order interaction terms CD:MFS, CD:SNH, and MFS:SNH at the 500 m spatial scale</td><td>27</td></tr><tr><td>Fig. S5. Correlation graphs between the second-order interaction terms CD:MFS, CD:SNH, and MFS:SNH at the 1000 m spatial scale</td><td>28</td></tr><tr><td>Fig. S6. Landcover maps of the 17 study sites</td><td>28</td></tr><tr><td>Fig. S7. Location and landscape composition (1,000 m scale) of the 17 study sites</td><td>29</td></tr><tr><td>Fig. S8. Relative effects of landscape predictors on the diversity and total abundance of all arthropods caught in rice fields (herbivores, predators, parasitoids and neutral insects)</td><td>30</td></tr><tr><td>Fig. S9. Relative importance of landscape predictors on rice herbivore abundance</td><td>31</td></tr><tr><td>Fig. S10. Relative importance of landscape predictors on predator abundance</td><td>32</td></tr><tr><td>Fig. S11. Relative importance of landscape predictors on parasitoid abundance</td><td>33</td></tr><tr><td>Fig. S12. Relative importance of landscape predictors on Shannon diversity of rice herbivores</td><td>34</td></tr><tr><td>Fig. S13. Relative importance of landscape predictors on Shannon diversity of predators</td><td>35</td></tr><tr><td>Fig. S14. Relative importance of landscape predictors on Shannon diversity of parasitoids</td><td>36</td></tr></table>
Supplementary tables

<table><caption>Table S1. Crop heterogeneity gradients in 17 rice landscapes.</caption><thead><tr><th rowspan="2">#</th><th rowspan="2">Village</th><th rowspan="2">Acron<br>ym</th><th rowspan="2">longitude</th><th rowspan="2">latitude</th><th rowspan="2">Size of<br>focal rice<br>field (ha)</th><th rowspan="2">% rice<br>area at<br>1000 m<br>scale</th><th colspan="4">Crop diversity<br>(Shannon-Wiener Index)</th><th colspan="4">Mean field size (hectares)</th><th colspan="4">Proportion of semi-natural<br>habitats</th></tr><tr><th>100 m</th><th>250 m</th><th>500 m</th><th>1000 m</th><th>100 m</th><th>250 m</th><th>500 m</th><th>1000 m</th><th>100 m</th><th>250 m</th><th>500 m</th><th>1000 m</th></tr></thead><tbody><tr><td>1</td><td>Nanxin Xia</td><td>NXX</td><td>110.839</td><td>19.719</td><td>0.0754</td><td>4.9</td><td>0.644</td><td>0.560</td><td>0.387</td><td>0.468</td><td>0.376</td><td>0.668</td><td>0.851</td><td>1.415</td><td>0.4811</td><td>0.5078</td><td>0.6657</td><td>0.7269</td></tr><tr><td>2</td><td>Baocai Nan</td><td>BCN</td><td>110.905</td><td>19.789</td><td>0.109</td><td>0.11</td><td>0.530</td><td>0.505</td><td>0.536</td><td>0.457</td><td>0.420</td><td>0.543</td><td>0.788</td><td>1.612</td><td>0.4676</td><td>0.6263</td><td>0.7520</td><td>0.7156</td></tr><tr><td>3</td><td>Feng Ming</td><td>FM</td><td>110.869</td><td>19.771</td><td>0.0609</td><td>0.50</td><td>0.357</td><td>0.615</td><td>0.451</td><td>0.419</td><td>1.441</td><td>1.609</td><td>1.131</td><td>1.812</td><td>0.5356</td><td>0.7040</td><td>0.7580</td><td>0.8149</td></tr><tr><td>4</td><td>Po Liu</td><td>PL</td><td>110.902</td><td>19.644</td><td>0.0672</td><td>5.9</td><td>0.293</td><td>0.362</td><td>0.223</td><td>0.213</td><td>1.964</td><td>5.988</td><td>7.440</td><td>1.147</td><td>0.3314</td><td>0.6199</td><td>0.6163</td><td>0.6543</td></tr><tr><td>5</td><td>Baodian Xi</td><td>BDX</td><td>110.872</td><td>19.682</td><td>0.0847</td><td>3.0</td><td>0.353</td><td>0.329</td><td>0.286</td><td>0.188</td><td>0.750</td><td>2.084</td><td>3.137</td><td>1.580</td><td>0.4899</td><td>0.7245</td><td>0.7945</td><td>0.8650</td></tr><tr><td>6</td><td>Tian Dui</td><td>TD</td><td>110.832</td><td>19.654</td><td>0.112</td><td>12.6</td><td>0.515</td><td>0.638</td><td>0.515</td><td>0.469</td><td>0.362</td><td>1.071</td><td>1.858</td><td>2.191</td><td>0.1335</td><td>0.4154</td><td>0.5738</td><td>0.7038</td></tr><tr><td>7</td><td>Song Shu</td><td>SS</td><td>110.788</td><td>19.656</td><td>0.0485</td><td>9.5</td><td>0.572</td><td>0.671</td><td>0.658</td><td>0.520</td><td>0.411</td><td>1.523</td><td>1.278</td><td>2.070</td><td>0.5780</td><td>0.5423</td><td>0.5689</td><td>0.6536</td></tr><tr><td>8</td><td>Xia Yuan</td><td>XY</td><td>110.752</td><td>19.639</td><td>0.0862</td><td>10.2</td><td>0.284</td><td>0.553</td><td>0.413</td><td>0.372</td><td>0.319</td><td>1.164</td><td>1.384</td><td>1.576</td><td>0.2166</td><td>0.3486</td><td>0.4708</td><td>0.5264</td></tr><tr><td>9</td><td>Dong Keng</td><td>DK</td><td>110.721</td><td>19.703</td><td>0.0535</td><td>4.8</td><td>0.283</td><td>0.200</td><td>0.305</td><td>0.352</td><td>0.466</td><td>1.545</td><td>0.962</td><td>1.657</td><td>0.6182</td><td>0.8279</td><td>0.7829</td><td>0.6684</td></tr><tr><td>10</td><td>Changshi Keng</td><td>CSK</td><td>110.692</td><td>19.756</td><td>0.0699</td><td>3.8</td><td>0.361</td><td>0.476</td><td>0.700</td><td>0.794</td><td>0.628</td><td>0.568</td><td>0.662</td><td>1.059</td><td>0.2578</td><td>0.6011</td><td>0.6494</td><td>0.5992</td></tr><tr><td>11</td><td>Mei De</td><td>MD</td><td>110.666</td><td>19.750</td><td>0.111</td><td>44.5</td><td>0.068</td><td>0.186</td><td>0.423</td><td>0.605</td><td>0.583</td><td>1.822</td><td>3.470</td><td>2.584</td><td>0.0000</td><td>0.0337</td><td>0.1126</td><td>0.3422</td></tr><tr><td>12</td><td>Shang Zhai</td><td>SZ</td><td>110.777</td><td>19.779</td><td>0.110</td><td>8.9</td><td>0.210</td><td>0.461</td><td>0.426</td><td>0.600</td><td>0.606</td><td>0.894</td><td>0.877</td><td>1.514</td><td>0.1114</td><td>0.4920</td><td>0.6477</td><td>0.6558</td></tr><tr><td>13</td><td>Long Dui</td><td>LD</td><td>110.740</td><td>19.838</td><td>0.0840</td><td>6.5</td><td>0.452</td><td>0.572</td><td>0.574</td><td>0.510</td><td>0.893</td><td>1.675</td><td>1.711</td><td>1.463</td><td>0.4309</td><td>0.7157</td><td>0.7117</td><td>0.6759</td></tr><tr><td>14</td><td>Shan Tou</td><td>ST</td><td>110.717</td><td>19.853</td><td>0.0776</td><td>2.1</td><td>0.616</td><td>0.932</td><td>0.687</td><td>0.407</td><td>0.196</td><td>0.673</td><td>0.891</td><td>1.315</td><td>0.4450</td><td>0.4880</td><td>0.6484</td><td>0.7296</td></tr><tr><td>15</td><td>Hou Po</td><td>HP</td><td>110.796</td><td>19.868</td><td>0.108</td><td>0.11</td><td>0.196</td><td>0.230</td><td>0.487</td><td>0.627</td><td>0.813</td><td>1.790</td><td>2.775</td><td>3.204</td><td>0.1305</td><td>0.2079</td><td>0.2166</td><td>0.3585</td></tr><tr><td>16</td><td>Paigang Zhong</td><td>PGZ</td><td>110.674</td><td>19.992</td><td>0.107</td><td>0.11</td><td>0.341</td><td>0.431</td><td>0.453</td><td>0.435</td><td>0.542</td><td>1.984</td><td>2.531</td><td>2.808</td><td>0.2560</td><td>0.3555</td><td>0.4636</td><td>0.5484</td></tr><tr><td>17</td><td>Hen Chan</td><td>HC</td><td>110.677</td><td>20.041</td><td>0.0445</td><td>0.05</td><td>0.360</td><td>0.368</td><td>0.295</td><td>0.148</td><td>0.459</td><td>3.459</td><td>4.257</td><td>2.810</td><td>0.2841</td><td>0.2160</td><td>0.5308</td><td>0.7080</td></tr></tbody><tfoot><tr><td colspan="2">Mean ± SE</td><td></td><td></td><td></td><td></td><td>0.083 ± 0.006</td><td>10.406 ± 2.990</td><td>0.379 ± 0.038</td><td>0.476 ± 0.046</td><td>0.460 ± 0.034</td><td>0.446 ± 0.040</td><td>0.661 ± 0.107</td><td>1.709 ± 0.320</td><td>2.118 ± 0.423</td><td>1.872 ± 0.154</td><td>0.339 ± 0.044</td><td>0.496 ± 0.052</td><td>0.586 ± 0.046</td><td>0.644 ± 0.033</td></tr></tfoot></table>
<table><caption>Table S2. The proportion of forest, grassland and shrubland in semi-natural habitats around each rice field across the four spatial scales.</caption><thead><tr><th>#</th><th>Village</th><th>Acronym</th><th>Scale</th><th>Forest</th><th>Grassland</th><th>Shrubland</th></tr></thead><tbody><tr><td>1</td><td>Nanxin Xia</td><td>NXX</td><td></td><td>0.4774</td><td>0.0012</td><td>0.0025</td></tr><tr><td>2</td><td>Baocai Nan</td><td>BCN</td><td></td><td>0.0931</td><td>0.3745</td><td>0</td></tr><tr><td>3</td><td>Feng Ming</td><td>FM</td><td></td><td>0.0365</td><td>0.4991</td><td>0</td></tr><tr><td>4</td><td>Po Liu</td><td>PL</td><td></td><td>0.0353</td><td>0.2961</td><td>0</td></tr><tr><td>5</td><td>Baodian Xi</td><td>BDX</td><td></td><td>0.1801</td><td>0.3098</td><td>0</td></tr><tr><td>6</td><td>Tian Dui</td><td>TD</td><td></td><td>0.1278</td><td>0.0057</td><td>0</td></tr><tr><td>7</td><td>Song Shu</td><td>SS</td><td></td><td>0.5130</td><td>0.0650</td><td>0</td></tr><tr><td>8</td><td>Xia Yuan</td><td>XY</td><td></td><td>0</td><td>0.2166</td><td>0</td></tr><tr><td>9</td><td>Dong Keng</td><td>DK</td><td>100 m</td><td>0.3670</td><td>0.2511</td><td>0</td></tr><tr><td>10</td><td>Changshi Keng</td><td>CSK</td><td></td><td>0</td><td>0.2578</td><td>0</td></tr><tr><td>11</td><td>Mei De</td><td>MD</td><td></td><td>0</td><td>0</td><td>0</td></tr><tr><td>12</td><td>Shang Zhai</td><td>SZ</td><td></td><td>0.1114</td><td>0</td><td>0</td></tr><tr><td>13</td><td>Long Dui</td><td>LD</td><td></td><td>0.1577</td><td>0.2733</td><td>0</td></tr><tr><td>14</td><td>Shan Tou</td><td>ST</td><td></td><td>0.0877</td><td>0.2982</td><td>0.0591</td></tr><tr><td>15</td><td>Hou Po</td><td>HP</td><td></td><td>0.0470</td><td>0.0835</td><td>0</td></tr><tr><td>16</td><td>Paigang Zhong</td><td>PGZ</td><td></td><td>0.1713</td><td>0.0847</td><td>0</td></tr><tr><td>17</td><td>Hen Chan</td><td>HC</td><td></td><td>0.2841</td><td>0</td><td>0</td></tr><tr><td>1</td><td>Nanxin Xia</td><td>NXX</td><td></td><td>0.4904</td><td>0.0102</td><td>0.0072</td></tr><tr><td>2</td><td>Baocai Nan</td><td>BCN</td><td></td><td>0.3374</td><td>0.2889</td><td>0</td></tr><tr><td>3</td><td>Feng Ming</td><td>FM</td><td></td><td>0.3776</td><td>0.3264</td><td>0</td></tr><tr><td>4</td><td>Po Liu</td><td>PL</td><td></td><td>0.2934</td><td>0.3266</td><td>0</td></tr><tr><td>5</td><td>Baodian Xi</td><td>BDX</td><td></td><td>0.4069</td><td>0.3177</td><td>0</td></tr><tr><td>6</td><td>Tian Dui</td><td>TD</td><td></td><td>0.3239</td><td>0.0915</td><td>0</td></tr><tr><td>7</td><td>Song Shu</td><td>SS</td><td></td><td>0.5124</td><td>0.0299</td><td>0</td></tr><tr><td>8</td><td>Xia Yuan</td><td>XY</td><td></td><td>0.0322</td><td>0.3073</td><td>0.0092</td></tr><tr><td>9</td><td>Dong Keng</td><td>DK</td><td>250 m</td><td>0.4166</td><td>0.4113</td><td>0</td></tr><tr><td>10</td><td>Changshi Keng</td><td>CSK</td><td></td><td>0.0281</td><td>0.5176</td><td>0.0555</td></tr><tr><td>11</td><td>Mei De</td><td>MD</td><td></td><td>0.0327</td><td>0.0011</td><td>0</td></tr><tr><td>12</td><td>Shang Zhai</td><td>SZ</td><td></td><td>0.3386</td><td>0.1533</td><td>0</td></tr><tr><td>13</td><td>Long Dui</td><td>LD</td><td></td><td>0.4586</td><td>0.2571</td><td>0</td></tr><tr><td>14</td><td>Shan Tou</td><td>ST</td><td></td><td>0.3530</td><td>0.0809</td><td>0.0541</td></tr><tr><td>15</td><td>Hou Po</td><td>HP</td><td></td><td>0.0199</td><td>0.1880</td><td>0</td></tr><tr><td>16</td><td>Paigang Zhong</td><td>PGZ</td><td></td><td>0.2630</td><td>0.0805</td><td>0.0120</td></tr><tr><td>17</td><td>Hen Chan</td><td>HC</td><td></td><td>0.0917</td><td>0.0344</td><td>0.0899</td></tr><tr><td>1</td><td>Nanxin Xia</td><td>NXX</td><td>500 m</td><td>0.4706</td><td>0.1819</td><td>0.0132</td></tr><tr><td>2</td><td>Baocai Nan</td><td>BCN</td><td></td><td>0.4558</td><td>0.2962</td><td>0</td></tr><tr><td>3</td><td>Feng Ming</td><td>FM</td><td></td><td>0.5149</td><td>0.2430</td><td>0</td></tr><tr><td>4</td><td>Po Liu</td><td>PL</td><td></td><td>0.4554</td><td>0.1599</td><td>0.0010</td></tr><tr><td>5</td><td>Baodian Xi</td><td>BDX</td><td></td><td>0.4166</td><td>0.3779</td><td>0</td></tr><tr><td>6</td><td>Tian Dui</td><td>TD</td><td></td><td>0.3621</td><td>0.2117</td><td>0</td></tr><tr><td>7</td><td>Song Shu</td><td>SS</td><td></td><td>0.5219</td><td>0.0337</td><td>0.0132</td></tr><tr><td>8</td><td>Xia Yuan</td><td>XY</td><td></td><td>0.1901</td><td>0.2698</td><td>0.0109</td></tr><tr><td>9</td><td>Dong Keng</td><td>DK</td><td></td><td>0.3828</td><td>0.4001</td><td>0</td></tr><tr><td>10</td><td>Changshi Keng</td><td>CSK</td><td></td><td>0.3092</td><td>0.3177</td><td>0.0225</td></tr><tr><td>11</td><td>Mei De</td><td>MD</td><td></td><td>0.0949</td><td>0.0177</td><td>0</td></tr><tr><td>12</td><td>Shang Zhai</td><td>SZ</td><td></td><td>0.4128</td><td>0.2329</td><td>0.0020</td></tr></tbody></table>
<table><caption>Table S3. Multicollinearity check between the three landscape predictors.</caption><thead><tr><th>#</th><th>Village</th><th>Acronym</th><th>Scale</th><th>Forest</th><th>Grassland</th><th>Shrubland</th></tr></thead><tbody><tr><td>13</td><td>Long Dui</td><td>LD</td><td></td><td>0.4852</td><td>0.2222</td><td>0.0043</td></tr><tr><td>14</td><td>Shan Tou</td><td>ST</td><td></td><td>0.4513</td><td>0.1648</td><td>0.0324</td></tr><tr><td>15</td><td>Hou Po</td><td>HP</td><td></td><td>0.1244</td><td>0.0902</td><td>0.0019</td></tr><tr><td>16</td><td>Paigang Zhong</td><td>PGZ</td><td></td><td>0.2579</td><td>0.1959</td><td>0.0098</td></tr><tr><td>17</td><td>Hen Chan</td><td>HC</td><td></td><td>0.1184</td><td>0.3548</td><td>0.0576</td></tr><tr><td>1</td><td>Nanxin Xia</td><td>NXX</td><td></td><td>0.4250</td><td>0.2888</td><td>0.0132</td></tr><tr><td>2</td><td>Baocai Nan</td><td>BCN</td><td></td><td>0.4787</td><td>0.2349</td><td>0.0020</td></tr><tr><td>3</td><td>Feng Ming</td><td>FM</td><td></td><td>0.4725</td><td>0.3370</td><td>0.0055</td></tr><tr><td>4</td><td>Po Liu</td><td>PL</td><td></td><td>0.3859</td><td>0.2628</td><td>0.0056</td></tr><tr><td>5</td><td>Baodian Xi</td><td>BDX</td><td></td><td>0.4856</td><td>0.3771</td><td>0.0023</td></tr><tr><td>6</td><td>Tian Dui</td><td>TD</td><td></td><td>0.3533</td><td>0.3457</td><td>0.0047</td></tr><tr><td>7</td><td>Song Shu</td><td>SS</td><td></td><td>0.4861</td><td>0.1530</td><td>0.0145</td></tr><tr><td>8</td><td>Xia Yuan</td><td>XY</td><td></td><td>0.3443</td><td>0.1572</td><td>0.0248</td></tr><tr><td>9</td><td>Dong Keng</td><td>DK</td><td>1000 m</td><td>0.3745</td><td>0.2939</td><td>0</td></tr><tr><td>10</td><td>Changshi Keng</td><td>CSK</td><td></td><td>0.2850</td><td>0.2723</td><td>0.0419</td></tr><tr><td>11</td><td>Mei De</td><td>MD</td><td></td><td>0.2393</td><td>0.1023</td><td>0.0005</td></tr><tr><td>12</td><td>Shang Zhai</td><td>SZ</td><td></td><td>0.3869</td><td>0.2546</td><td>0.0143</td></tr><tr><td>13</td><td>Long Dui</td><td>LD</td><td></td><td>0.4207</td><td>0.2409</td><td>0.0144</td></tr><tr><td>14</td><td>Shan Tou</td><td>ST</td><td></td><td>0.5493</td><td>0.1627</td><td>0.0176</td></tr><tr><td>15</td><td>Hou Po</td><td>HP</td><td></td><td>0.2403</td><td>0.1080</td><td>0.0102</td></tr><tr><td>16</td><td>Paigang Zhong</td><td>PGZ</td><td></td><td>0.2661</td><td>0.2595</td><td>0.0228</td></tr><tr><td>17</td><td>Hen Chan</td><td>HC</td><td></td><td>0.1900</td><td>0.4781</td><td>0.0399</td></tr></tbody></table>

Analyses of collinearity between the three landscape predictors studied for each spatial scale (CD: crop diversity; MFS: mean field size; SNH: semi-natural habitat proportion).

<table><thead><tr><th>Spatial scale</th><th>Results</th><th>Variable</th><th>VIF</th><th>VIF 95% CI</th><th>Increased SE</th><th>Tolerance</th><th>Tolerance 95% CI</th></tr></thead><tbody><tr><td rowspan="4">100 m</td><td rowspan="4">Low Correlation</td><td>CD</td><td>1.91</td><td>[1.43, 2.91]</td><td>1.38</td><td>0.52</td><td>[0.34, 0.70]</td></tr><tr><td>MFS</td><td>1.25</td><td>[1.06, 2.06]</td><td>1.12</td><td>0.8</td><td>[0.49, 0.94]</td></tr><tr><td>SNH</td><td>1.71</td><td>[1.31, 2.62]</td><td>1.31</td><td>0.58</td><td>[0.38, 0.76]</td></tr><tr><td>CD</td><td>1.22</td><td>[1.05, 2.06]</td><td>1.11</td><td>0.82</td><td>[0.49, 0.96]</td></tr><tr><td rowspan="4">250 m</td><td rowspan="4">Low Correlation</td><td>MFS</td><td>1.17</td><td>[1.03, 2.13]</td><td>1.08</td><td>0.86</td><td>[0.47, 0.98]</td></tr><tr><td>SNH</td><td>1.05</td><td>[1.00, 12.01]</td><td>1.02</td><td>0.95</td><td>[0.08, 1.00]</td></tr><tr><td>CD</td><td>1.74</td><td>[1.33, 2.66]</td><td>1.32</td><td>0.58</td><td>[0.38, 0.75]</td></tr><tr><td>MFS</td><td>1.91</td><td>[1.43, 2.92]</td><td>1.38</td><td>0.52</td><td>[0.34, 0.70]</td></tr><tr><td rowspan="4">500 m</td><td rowspan="4">Low Correlation</td><td>SNH</td><td>1.19</td><td>[1.03, 2.09]</td><td>1.09</td><td>0.84</td><td>[0.48, 0.97]</td></tr><tr><td>CD</td><td>1.53</td><td>[1.21, 2.36]</td><td>1.24</td><td>0.65</td><td>[0.42, 0.83]</td></tr><tr><td>MFS</td><td>1.58</td><td>[1.24, 2.42]</td><td>1.26</td><td>0.63</td><td>[0.41, 0.81]</td></tr><tr><td>SNH</td><td>2.13</td><td>[1.56, 3.26]</td><td>1.46</td><td>0.47</td><td>[0.31, 0.64]</td></tr><tr><td>1000 m</td><td>Low Correlation</td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>
**Table S4. Tested models for each spatial scale.**

Landscape predictors included in tested models for the total abundance or Shannon Index of herbivores, predators and parasitoids at each spatial scale. When landscape predictors were significantly correlated at a given spatial scale, they were included in alternative models 1 and 2. CD: crop diversity; MFS: mean field size; SNH: semi-natural habitat proportion.

<table><thead><tr><th>Scale</th><th>Tested models</th></tr></thead><tbody><tr><td rowspan="2">100 m</td><td>model 1 &lt;- CD + MFS + CD:MFS</td></tr><tr><td>model 2 &lt;- SNH + MFS + SNH:MFS</td></tr><tr><td rowspan="2">250 m</td><td>model 1 &lt;- CD*MFS*SNH</td></tr><tr><td>model 1 &lt;- CD + SNH + CD:SNH</td></tr><tr><td rowspan="2">500 m</td><td>model 2 &lt;- MFS + SNH + MFS:SNH</td></tr><tr><td>model 1 &lt;- CD + MFS + CD:MFS</td></tr><tr><td>1000 m</td><td>model 2 &lt;- SNH</td></tr></tbody></table>
<caption>Table S5. Results of excessive discreteness tests at each spatial scale and for each response variable.</caption>

Note: “model 1” and “model 2” are detailed in Table S2.

<table><thead><tr><th>Response variable</th><th>Spatial scale</th><th>Alternative model</th><th>Chisq</th><th>Ratio</th><th>rdf</th><th>P</th></tr></thead><tbody><tr><td rowspan="6">Herbivore abundance</td><td rowspan="2">100 m</td><td>model 1</td><td>20.07</td><td>0.45</td><td>45</td><td>1.00</td></tr><tr><td>model 2</td><td>19.91</td><td>0.44</td><td>45</td><td>1.00</td></tr><tr><td rowspan="2">250 m</td><td>model 1</td><td>19.39</td><td>0.47</td><td>41</td><td>1.00</td></tr><tr><td>model 2</td><td>20.16</td><td>0.45</td><td>45</td><td>1.00</td></tr><tr><td rowspan="2">500 m</td><td>model 1</td><td>20.26</td><td>0.45</td><td>45</td><td>1.00</td></tr><tr><td>model 2</td><td>20.44</td><td>0.45</td><td>45</td><td>1.00</td></tr><tr><td rowspan="2">1000 m</td><td>model 1</td><td>20.78</td><td>0.44</td><td>47</td><td>1.00</td></tr><tr><td>model 2</td><td>30.92</td><td>0.69</td><td>45</td><td>0.95</td></tr><tr><td rowspan="6">Parasitoid abundance</td><td rowspan="2">100 m</td><td>model 1</td><td>30.98</td><td>0.69</td><td>45</td><td>0.94</td></tr><tr><td>model 2</td><td>30.70</td><td>0.75</td><td>41</td><td>0.88</td></tr><tr><td rowspan="2">250 m</td><td>model 1</td><td>34.02</td><td>0.76</td><td>45</td><td>0.88</td></tr><tr><td>model 2</td><td>31.31</td><td>0.70</td><td>45</td><td>0.94</td></tr><tr><td rowspan="2">500 m</td><td>model 1</td><td>32.37</td><td>0.72</td><td>45</td><td>0.92</td></tr><tr><td>model 2</td><td>32.24</td><td>0.69</td><td>47</td><td>0.95</td></tr><tr><td rowspan="6">Predator abundance</td><td rowspan="2">100 m</td><td>model 1</td><td>34.41</td><td>0.76</td><td>45</td><td>0.87</td></tr><tr><td>model 2</td><td>35.32</td><td>0.78</td><td>45</td><td>0.85</td></tr><tr><td rowspan="2">250 m</td><td>model 1</td><td>35.12</td><td>0.86</td><td>41</td><td>0.73</td></tr><tr><td>model 2</td><td>34.24</td><td>0.76</td><td>45</td><td>0.88</td></tr><tr><td rowspan="2">500 m</td><td>model 1</td><td>35.27</td><td>0.78</td><td>45</td><td>0.85</td></tr><tr><td>model 2</td><td>36.04</td><td>0.80</td><td>45</td><td>0.83</td></tr><tr><td rowspan="2">1000 m</td><td>model 1</td><td>35.72</td><td>0.76</td><td>47</td><td>0.89</td></tr><tr><td>model 2</td><td>31.04</td><td>0.69</td><td>45</td><td>0.94</td></tr><tr><td rowspan="6">Herbivore diversity</td><td rowspan="2">100 m</td><td>model 1</td><td>30.65</td><td>0.68</td><td>45</td><td>0.95</td></tr><tr><td>model 2</td><td>29.57</td><td>0.72</td><td>41</td><td>0.91</td></tr><tr><td rowspan="2">250 m</td><td>model 1</td><td>31.14</td><td>0.69</td><td>45</td><td>0.94</td></tr><tr><td>model 2</td><td>30.95</td><td>0.69</td><td>45</td><td>0.95</td></tr><tr><td rowspan="2">500 m</td><td>model 1</td><td>33.48</td><td>0.74</td><td>45</td><td>0.90</td></tr><tr><td>model 2</td><td>31.92</td><td>0.68</td><td>47</td><td>0.95</td></tr><tr><td rowspan="6">Parasitoid diversity</td><td rowspan="2">100 m</td><td>model 1</td><td>37.84</td><td>0.84</td><td>45</td><td>0.77</td></tr><tr><td>model 2</td><td>39.07</td><td>0.87</td><td>45</td><td>0.72</td></tr><tr><td rowspan="2">250 m</td><td>model 1</td><td>35.62</td><td>0.87</td><td>41</td><td>0.71</td></tr><tr><td>model 2</td><td>37.80</td><td>0.84</td><td>45</td><td>0.77</td></tr><tr><td rowspan="2">500 m</td><td>model 1</td><td>37.78</td><td>0.84</td><td>45</td><td>0.77</td></tr><tr><td>model 2</td><td>38.27</td><td>0.85</td><td>45</td><td>0.75</td></tr><tr><td rowspan="2">1000 m</td><td>model 1</td><td>39.13</td><td>0.83</td><td>47</td><td>0.79</td></tr><tr><td>model 2</td><td>32.58</td><td>0.72</td><td>45</td><td>0.92</td></tr><tr><td rowspan="6">Predator diversity</td><td rowspan="2">100 m</td><td>model 1</td><td>34.17</td><td>0.76</td><td>45</td><td>0.88</td></tr><tr><td>model 2</td><td>32.90</td><td>0.80</td><td>41</td><td>0.81</td></tr><tr><td rowspan="2">250 m</td><td>model 1</td><td>32.59</td><td>0.72</td><td>45</td><td>0.92</td></tr><tr><td>model 2</td><td>32.98</td><td>0.73</td><td>45</td><td>0.91</td></tr><tr><td rowspan="2">500 m</td><td>model 1</td><td>33.32</td><td>0.74</td><td>45</td><td>0.90</td></tr><tr><td>model 2</td><td>34.22</td><td>0.73</td><td>47</td><td>0.92</td></tr><tr><td rowspan="6">Predator diversity</td><td rowspan="2">1000 m</td><td>model 1</td><td>32.58</td><td>0.72</td><td>45</td><td>0.92</td></tr><tr><td>model 2</td><td>34.17</td><td>0.76</td><td>45</td><td>0.88</td></tr></tbody></table>
Table S6. Estimates and model weights.

ΔAICc < 7 for the four spatial scales (100m, 250m, 500m, 1000m) for abundance of herbivore, predator and parasitoid, and Shannon diversity of herbivores, predators and parasitoids. Models are listed in descending order according to their ΔAICc. Standardized parameter estimates, degrees of freedom (df), Akaike's Information Criterion with small-sample size adjustment (AICc), ΔAICc and Akaike weights (wi) of each explanatory variable for the set of top models for each scale based on model averaging are reported. ‘NA’ parameters not selected in set of top models. The space indicated that the model doesn't select this landscape factor. CD: crop diversity; MFS: mean field size; SNH: semi-natural habitats. Estimates with shaded with gray color indicated that this landscape factor was selected in model 2 again. The AICc value with blue color corresponds to the minimum AICc in this model.

<table><thead><tr><th></th><th></th><th></th><th>(Intercept)</th><th>CD</th><th>MFS</th><th>SNH</th><th>CD: MFS</th><th>CD: SNH</th><th>MFS:SNH</th><th>CD:MFS: SNH</th><th>df</th><th>logLik</th><th>AICc</th><th>delta</th><th>weight</th></tr></thead><tbody><tr><td rowspan="6">Herbivore Abundance</td><td rowspan="5">100 m</td><td>model 1</td><td>1</td><td>0</td><td>NA</td><td>NA</td><td>NA</td><td></td><td></td><td></td><td></td><td>3</td><td>-67.862</td><td>140.813</td><td>0</td><td>0.500</td></tr><tr><td></td><td>2</td><td>0</td><td>0.191</td><td>NA</td><td>NA</td><td></td><td></td><td></td><td></td><td>4</td><td>-68.097</td><td>142.233</td><td>1.420</td><td>0.246</td></tr><tr><td></td><td>3</td><td>0</td><td>NA</td><td>0.016</td><td>NA</td><td></td><td></td><td></td><td></td><td>4</td><td>-68.538</td><td>143.232</td><td>2.419</td><td>0.149</td></tr><tr><td></td><td>4</td><td>0</td><td>0.219</td><td>0.087</td><td>NA</td><td></td><td></td><td></td><td></td><td>5</td><td>-68.639</td><td>144.625</td><td>3.812</td><td>0.074</td></tr><tr><td></td><td>8</td><td>-0.128</td><td>0.027</td><td>-0.035</td><td>-0.406</td><td></td><td></td><td></td><td></td><td>6</td><td>-68.106</td><td>146.405</td><td>5.592</td><td>0.031</td></tr><tr><td rowspan="5">model 2</td><td>1</td><td>0</td><td></td><td>NA</td><td>NA</td><td></td><td></td><td></td><td></td><td></td><td>3</td><td>-67.862</td><td>140.813</td><td>0</td><td>0.552</td></tr><tr><td>3</td><td>0</td><td></td><td>NA</td><td>0.134</td><td></td><td></td><td></td><td></td><td></td><td>4</td><td>-68.325</td><td>142.749</td><td>1.935</td><td>0.210</td></tr><tr><td>2</td><td>0</td><td></td><td>0.016</td><td>NA</td><td></td><td></td><td></td><td></td><td></td><td>4</td><td>-68.538</td><td>143.232</td><td>2.419</td><td>0.165</td></tr><tr><td>4</td><td>0</td><td></td><td>0.008</td><td>0.134</td><td></td><td></td><td></td><td></td><td></td><td>5</td><td>-68.983</td><td>145.332</td><td>4.519</td><td>0.058</td></tr><tr><td>8</td><td>0.005</td><td></td><td>0.030</td><td>0.120</td><td></td><td></td><td></td><td></td><td></td><td>6</td><td>-69.044</td><td>148.022</td><td>7.209</td><td>0.015</td></tr><tr><td rowspan="3">250 m</td><td>model 1</td><td>1</td><td>0</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>3</td><td>-67.862</td><td>140.813</td><td>0</td><td>0.255</td></tr><tr><td></td><td>5</td><td>0</td><td>NA</td><td>NA</td><td>0.261</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>4</td><td>-67.689</td><td>141.307</td><td>0.493</td><td>0.199</td></tr><tr><td></td><td>2</td><td>0</td><td>0.218</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>4</td><td>-67.956</td><td>141.912</td><td>1.099</td><td>0.147</td></tr></tbody></table>
<table><thead><tr><th></th><th></th><th>(Intercept)</th><th>CD</th><th>MFS</th><th>SNH</th><th>CD: MFS</th><th>CD: SNH</th><th>MFS:SNH</th><th>CD:MFS: SNH</th><th>df</th><th>logLik</th><th>AICc</th><th>delta</th><th>weight</th></tr></thead><tbody><tr><td rowspan="12">500 m</td><td rowspan="12">model 1</td><td>6</td><td>0</td><td>0.170</td><td>NA</td><td>0.225</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>5</td><td>-68.017</td><td>143.038</td><td>2.225</td><td>0.084</td></tr><tr><td>3</td><td>0</td><td>NA</td><td>-0.017</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>4</td><td>-68.537</td><td>143.231</td><td>2.418</td><td>0.076</td></tr><tr><td>7</td><td>0</td><td>NA</td><td>-0.005</td><td>0.261</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>5</td><td>-68.390</td><td>143.891</td><td>3.078</td><td>0.055</td></tr><tr><td>4</td><td>0</td><td>0.247</td><td>0.076</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>5</td><td>-68.503</td><td>144.352</td><td>3.539</td><td>0.043</td></tr><tr><td>12</td><td>-0.236</td><td>-0.011</td><td>-0.233</td><td>NA</td><td>-0.635</td><td>NA</td><td>NA</td><td>NA</td><td>6</td><td>-67.504</td><td>145.328</td><td>4.515</td><td>0.027</td></tr><tr><td>16</td><td>-0.309</td><td>-0.159</td><td>-0.339</td><td>0.303</td><td>-0.830</td><td>NA</td><td>NA</td><td>NA</td><td>7</td><td>-67.094</td><td>145.392</td><td>4.579</td><td>0.026</td></tr><tr><td>8</td><td>0</td><td>0.197</td><td>0.068</td><td>0.222</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>6</td><td>-68.583</td><td>145.675</td><td>4.861</td><td>0.022</td></tr><tr><td>22</td><td>0.012</td><td>0.169</td><td>NA</td><td>0.176</td><td>NA</td><td>-0.057</td><td>NA</td><td>NA</td><td>6</td><td>-68.431</td><td>145.740</td><td>4.926</td><td>0.022</td></tr><tr><td>39</td><td>0.012</td><td>NA</td><td>-0.082</td><td>0.206</td><td>NA</td><td>NA</td><td>0.255</td><td>NA</td><td>6</td><td>-68.265</td><td>145.948</td><td>5.135</td><td>0.020</td></tr><tr><td>40</td><td>0.010</td><td>0.172</td><td>-0.004</td><td>0.183</td><td>NA</td><td>NA</td><td>0.207</td><td>NA</td><td>7</td><td>-68.534</td><td>148.162</td><td>7.349</td><td>0.006</td></tr><tr><td>48</td><td>-0.291</td><td>-0.157</td><td>-0.359</td><td>0.279</td><td>-0.795</td><td>NA</td><td>0.105</td><td>NA</td><td>8</td><td>-67.199</td><td>148.436</td><td>7.623</td><td>0.006</td></tr><tr><td>32</td><td>-0.321</td><td>-0.165</td><td>-0.346</td><td>0.332</td><td>-0.844</td><td>0.032</td><td>NA</td><td>NA</td><td>8</td><td>-67.519</td><td>148.552</td><td>7.738</td><td>0.005</td></tr><tr><td>24</td><td>0.011</td><td>0.195</td><td>0.067</td><td>0.176</td><td>NA</td><td>-0.054</td><td>NA</td><td>NA</td><td>7</td><td>-68.965</td><td>148.578</td><td>7.765</td><td>0.005</td></tr><tr><td>56</td><td>0.022</td><td>0.170</td><td>-0.005</td><td>0.135</td><td>NA</td><td>-0.056</td><td>0.208</td><td>NA</td><td>8</td><td>-68.889</td><td>151.282</td><td>10.469</td><td>0.001</td></tr><tr><td>64</td><td>-0.302</td><td>-0.162</td><td>-0.364</td><td>0.305</td><td>-0.809</td><td>0.028</td><td>0.103</td><td>NA</td><td>9</td><td>-67.583</td><td>151.855</td><td>11.041</td><td>0.001</td></tr><tr><td>128</td><td>-0.318</td><td>-0.167</td><td>-0.363</td><td>0.365</td><td>-0.829</td><td>0.061</td><td>0.263</td><td>0.335</td><td>10</td><td>-66.154</td><td>155.507</td><td>14.694</td><td>0</td></tr><tr><td rowspan="6">model 2</td><td>3</td><td>0</td><td>NA</td><td></td><td>0.308</td><td>NA</td><td></td><td></td><td></td><td>4</td><td>-67.328</td><td>140.489</td><td>0</td><td>0.404</td></tr><tr><td>1</td><td>0</td><td>NA</td><td></td><td>NA</td><td>NA</td><td></td><td></td><td></td><td>3</td><td>-67.862</td><td>140.813</td><td>0.324</td><td>0.344</td></tr><tr><td>4</td><td>0</td><td>0.049</td><td></td><td>0.309</td><td>NA</td><td></td><td></td><td></td><td>5</td><td>-68.024</td><td>142.998</td><td>2.510</td><td>0.115</td></tr><tr><td>2</td><td>0</td><td>0.043</td><td></td><td>NA</td><td>NA</td><td></td><td></td><td></td><td>4</td><td>-68.519</td><td>143.190</td><td>2.701</td><td>0.105</td></tr><tr><td>8</td><td>0.003</td><td>-0.004</td><td></td><td>0.338</td><td>NA</td><td>0.163</td><td></td><td></td><td>6</td><td>-67.842</td><td>145.593</td><td>5.104</td><td>0.032</td></tr><tr><td>3</td><td>0</td><td>NA</td><td></td><td>0.308</td><td>NA</td><td></td><td></td><td></td><td>4</td><td>-67.328</td><td>140.489</td><td>0</td><td>0.397</td></tr><tr><td rowspan="3">model 2</td><td>1</td><td>0</td><td>NA</td><td></td><td>NA</td><td>NA</td><td></td><td></td><td></td><td>3</td><td>-67.862</td><td>140.813</td><td>0.324</td><td>0.337</td></tr><tr><td>4</td><td>0</td><td>0.066</td><td></td><td>0.328</td><td>NA</td><td></td><td></td><td></td><td>5</td><td>-67.955</td><td>142.948</td><td>2.460</td><td>0.116</td></tr><tr><td>2</td><td>0</td><td>-0.033</td><td></td><td>NA</td><td>NA</td><td></td><td></td><td></td><td>4</td><td>-68.528</td><td>143.209</td><td>2.720</td><td>0.102</td></tr></tbody></table>
<table><thead><tr><th></th><th></th><th></th><th>(Intercept)</th><th>CD</th><th>MFS</th><th>SNH</th><th>CD: MFS</th><th>CD: SNH</th><th>MFS:SNH</th><th>CD:MFS: SNH</th><th>df</th><th>logLik</th><th>AICc</th><th>delta</th><th>weight</th></tr></thead><tbody><tr><td></td><td></td><td>8</td><td>-0.115</td><td></td><td>0.149</td><td>0.475</td><td></td><td>-0.387</td><td></td><td></td><td>6</td><td>-67.474</td><td>144.688</td><td>4.199</td><td>0.049</td></tr><tr><td rowspan="6">1000 m</td><td rowspan="6">model 1</td><td>3</td><td>0</td><td>NA</td><td>-0.411</td><td></td><td>NA</td><td></td><td></td><td></td><td></td><td>4</td><td>-66.212</td><td>137.960</td><td>0</td><td>0.602</td></tr><tr><td>4</td><td>0</td><td>-0.008</td><td>-0.411</td><td></td><td>NA</td><td></td><td></td><td></td><td></td><td>5</td><td>-67.012</td><td>140.543</td><td>2.583</td><td>0.165</td></tr><tr><td>1</td><td>0</td><td>NA</td><td>NA</td><td></td><td>NA</td><td></td><td></td><td></td><td></td><td>3</td><td>-67.862</td><td>140.813</td><td>2.853</td><td>0.145</td></tr><tr><td>8</td><td>0.001</td><td>-0.013</td><td>-0.400</td><td></td><td>-0.052</td><td></td><td></td><td></td><td></td><td>6</td><td>-67.928</td><td>143.154</td><td>5.194</td><td>0.045</td></tr><tr><td>2</td><td>0</td><td>-0.013</td><td>NA</td><td></td><td>NA</td><td></td><td></td><td></td><td></td><td>4</td><td>-68.539</td><td>143.235</td><td>5.275</td><td>0.043</td></tr><tr><td>2</td><td>0</td><td></td><td></td><td>0.324</td><td></td><td></td><td></td><td></td><td></td><td>4</td><td>-67.183</td><td>140.160</td><td>0</td><td>0.581</td></tr><tr><td></td><td></td><td>1</td><td>0</td><td></td><td></td><td>NA</td><td></td><td></td><td></td><td></td><td>3</td><td>-67.862</td><td>140.813</td><td>0.653</td><td>0.419</td></tr></tbody></table>

Predator Abudance

<table><tbody><tr><td rowspan="6">100 m</td><td rowspan="6">model 1</td><td>1</td><td>0</td><td>NA</td><td>NA</td><td></td><td>NA</td><td></td><td></td><td></td><td>3</td><td>-72.260</td><td>149.226</td><td>0</td><td>0.572</td></tr><tr><td>3</td><td>0</td><td>NA</td><td>-0.040</td><td></td><td>NA</td><td></td><td></td><td></td><td>4</td><td>-73.104</td><td>151.588</td><td>2.362</td><td>0.176</td></tr><tr><td>2</td><td>0</td><td>-0.040</td><td>NA</td><td></td><td>NA</td><td></td><td></td><td></td><td>4</td><td>-73.104</td><td>151.589</td><td>2.363</td><td>0.176</td></tr><tr><td>4</td><td>0</td><td>-0.059</td><td>-0.059</td><td></td><td>NA</td><td></td><td></td><td></td><td>5</td><td>-73.838</td><td>154.049</td><td>4.823</td><td>0.051</td></tr><tr><td>8</td><td>-0.126</td><td>-0.246</td><td>-0.178</td><td></td><td>-0.397</td><td></td><td></td><td></td><td>6</td><td>-73.343</td><td>155.502</td><td>6.276</td><td>0.025</td></tr><tr><td>1</td><td>0</td><td></td><td>NA</td><td>NA</td><td></td><td>NA</td><td></td><td></td><td>3</td><td>-72.260</td><td>149.226</td><td>0</td><td>0.544</td></tr><tr><td rowspan="4">model 2</td><td>2</td><td>0</td><td></td><td>-0.040</td><td>NA</td><td></td><td>NA</td><td></td><td></td><td>4</td><td>-73.104</td><td>151.588</td><td>2.362</td><td>0.167</td></tr><tr><td>3</td><td>0</td><td></td><td>NA</td><td>0.023</td><td></td><td>NA</td><td></td><td></td><td>4</td><td>-73.122</td><td>151.630</td><td>2.404</td><td>0.164</td></tr><tr><td>8</td><td>0.029</td><td></td><td>0.085</td><td>-0.057</td><td></td><td>-0.503</td><td></td><td></td><td>6</td><td>-72.740</td><td>153.090</td><td>3.864</td><td>0.079</td></tr><tr><td>4</td><td>0</td><td></td><td>-0.042</td><td>0.026</td><td></td><td>NA</td><td></td><td></td><td>5</td><td>-73.931</td><td>154.147</td><td>4.921</td><td>0.046</td></tr></tbody></table>

<table><tbody><tr><td rowspan="6">250 m</td><td rowspan="6">model 1</td><td>2</td><td>0</td><td>-0.242</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>4</td><td>-72.042</td><td>149.181</td><td>0</td><td>0.184</td></tr><tr><td>1</td><td>0</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>3</td><td>-72.260</td><td>149.226</td><td>0.045</td><td>0.180</td></tr><tr><td>3</td><td>0</td><td>NA</td><td>0.182</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>4</td><td>-72.533</td><td>150.294</td><td>1.114</td><td>0.106</td></tr><tr><td>48</td><td>-0.335</td><td>-0.483</td><td>-0.114</td><td>0.138</td><td>-0.832</td><td>NA</td><td>-0.540</td><td>NA</td><td>8</td><td>-70.599</td><td>150.790</td><td>1.609</td><td>0.082</td></tr><tr><td>12</td><td>-0.240</td><td>-0.464</td><td>-0.208</td><td>NA</td><td>-0.644</td><td>NA</td><td>NA</td><td>NA</td><td>6</td><td>-71.449</td><td>151.047</td><td>1.866</td><td>0.072</td></tr></tbody></table>
<table><thead><tr><th></th><th></th><th></th><th>(Intercept)</th><th>CD</th><th>MFS</th><th>SNH</th><th>CD: MFS</th><th>CD: SNH</th><th>MFS:SNH</th><th>CD:MFS: SNH</th><th>df</th><th>logLik</th><th>AICc</th><th>delta</th><th>weight</th></tr></thead><tbody><tr><td rowspan="24">500 m</td><td rowspan="12">model 1</td><td>5</td><td>0</td><td>NA</td><td>NA</td><td>-0.091</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>4</td><td>-72.987</td><td>151.323</td><td>2.142</td><td>0.063</td></tr><tr><td>4</td><td>0</td><td>-0.202</td><td>0.105</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>5</td><td>-72.694</td><td>151.326</td><td>2.145</td><td>0.063</td></tr><tr><td>39</td><td>-0.023</td><td>NA</td><td>0.320</td><td>0.018</td><td>NA</td><td>NA</td><td>-0.472</td><td>NA</td><td>6</td><td>-72.168</td><td>151.555</td><td>2.374</td><td>0.056</td></tr><tr><td>6</td><td>0</td><td>-0.233</td><td>NA</td><td>-0.041</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>5</td><td>-72.899</td><td>151.691</td><td>2.510</td><td>0.052</td></tr><tr><td>7</td><td>0</td><td>NA</td><td>0.178</td><td>-0.082</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>5</td><td>-73.291</td><td>152.590</td><td>3.409</td><td>0.034</td></tr><tr><td>40</td><td>-0.021</td><td>-0.139</td><td>0.257</td><td>0.037</td><td>NA</td><td>NA</td><td>-0.433</td><td>NA</td><td>7</td><td>-72.673</td><td>153.660</td><td>4.479</td><td>0.020</td></tr><tr><td>16</td><td>-0.244</td><td>-0.473</td><td>-0.215</td><td>0.019</td><td>-0.656</td><td>NA</td><td>NA</td><td>NA</td><td>7</td><td>-72.326</td><td>153.989</td><td>4.808</td><td>0.017</td></tr><tr><td>8</td><td>0</td><td>-0.191</td><td>0.107</td><td>-0.045</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>6</td><td>-73.524</td><td>153.998</td><td>4.817</td><td>0.017</td></tr><tr><td>64</td><td>-0.352</td><td>-0.490</td><td>-0.122</td><td>0.177</td><td>-0.852</td><td>0.043</td><td>-0.544</td><td>NA</td><td>9</td><td>-71.333</td><td>154.140</td><td>4.960</td><td>0.015</td></tr><tr><td>22</td><td>0.011</td><td>-0.234</td><td>NA</td><td>-0.087</td><td>NA</td><td>-0.053</td><td>NA</td><td>NA</td><td>6</td><td>-73.492</td><td>154.376</td><td>5.195</td><td>0.014</td></tr><tr><td>128</td><td>-0.278</td><td>-0.466</td><td>-0.125</td><td>-0.090</td><td>-0.761</td><td>-0.105</td><td>-1.257</td><td>-1.491</td><td>10</td><td>-69.376</td><td>154.391</td><td>5.211</td><td>0.014</td></tr><tr><td>56</td><td>-0.011</td><td>-0.141</td><td>0.255</td><td>-0.002</td><td>NA</td><td>-0.045</td><td>-0.432</td><td>NA</td><td>8</td><td>-73.302</td><td>156.771</td><td>7.590</td><td>0.004</td></tr><tr><td>24</td><td>0.010</td><td>-0.193</td><td>0.106</td><td>-0.087</td><td>NA</td><td>-0.049</td><td>NA</td><td>NA</td><td>7</td><td>-74.097</td><td>156.889</td><td>7.708</td><td>0.004</td></tr><tr><td>32</td><td>-0.251</td><td>-0.476</td><td>-0.219</td><td>0.036</td><td>-0.664</td><td>0.019</td><td>NA</td><td>NA</td><td>8</td><td>-72.942</td><td>157.160</td><td>7.979</td><td>0.003</td></tr><tr><td rowspan="12">model 2</td><td>1</td><td>0</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>3</td><td>-72.260</td><td>149.226</td><td>0</td><td>0.520</td></tr><tr><td>2</td><td>0</td><td>-0.142</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>4</td><td>-72.771</td><td>150.834</td><td>1.608</td><td>0.233</td></tr><tr><td>3</td><td>0</td><td>NA</td><td>-0.050</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>4</td><td>-73.089</td><td>151.555</td><td>2.329</td><td>0.162</td></tr><tr><td>4</td><td>0</td><td>-0.143</td><td>-0.052</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>5</td><td>-73.587</td><td>153.306</td><td>4.080</td><td>0.068</td></tr><tr><td>8</td><td>-0.002</td><td>-0.111</td><td>-0.071</td><td>-0.100</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>6</td><td>-73.574</td><td>155.984</td><td>6.758</td><td>0.018</td></tr><tr><td>1</td><td>0</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>3</td><td>-72.260</td><td>149.226</td><td>0</td><td>0.370</td></tr><tr><td>2</td><td>0</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>4</td><td>-72.078</td><td>149.262</td><td>0.036</td><td>0.363</td></tr><tr><td>3</td><td>0</td><td>NA</td><td>-0.050</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>4</td><td>-73.089</td><td>151.555</td><td>2.329</td><td>0.115</td></tr><tr><td>4</td><td>0</td><td>NA</td><td>0.245</td><td>0.025</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>5</td><td>-72.928</td><td>151.822</td><td>2.596</td><td>0.101</td></tr><tr><td>8</td><td>-0.111</td><td>NA</td><td>0.326</td><td>0.167</td><td>-0.374</td><td>NA</td><td>NA</td><td>NA</td><td>6</td><td>-72.489</td><td>153.205</td><td>3.979</td><td>0.051</td></tr><tr><td>1000 m</td><td>model 1</td><td>4</td><td>0</td><td>-0.238</td><td>0.257</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>5</td><td>-71.664</td><td>148.637</td><td>0</td><td>0.279</td></tr></tbody></table>
<table><thead><tr><th></th><th></th><th>(Intercept)</th><th>CD</th><th>MFS</th><th>SNH</th><th>CD: MFS</th><th>CD: SNH</th><th>MFS:SNH</th><th>CD:MFS: SNH</th><th>df</th><th>logLik</th><th>AICc</th><th>delta</th><th>weight</th></tr></thead><tbody><tr><td></td><td>3</td><td>0</td><td>NA</td><td>0.254</td><td></td><td>NA</td><td></td><td></td><td></td><td>4</td><td>-71.919</td><td>148.903</td><td>0.266</td><td>0.244</td></tr><tr><td></td><td>1</td><td>0</td><td>NA</td><td>NA</td><td></td><td>NA</td><td></td><td></td><td></td><td>3</td><td>-72.260</td><td>149.226</td><td>0.589</td><td>0.208</td></tr><tr><td></td><td>2</td><td>0</td><td>-0.235</td><td>NA</td><td></td><td>NA</td><td></td><td></td><td></td><td>4</td><td>-72.105</td><td>149.323</td><td>0.687</td><td>0.198</td></tr><tr><td></td><td>8</td><td>0</td><td>-0.238</td><td>0.256</td><td></td><td>0.002</td><td></td><td></td><td></td><td>6</td><td>-72.839</td><td>151.398</td><td>2.761</td><td>0.070</td></tr><tr><td rowspan="2">model 2</td><td>1</td><td>0</td><td></td><td></td><td>NA</td><td></td><td></td><td></td><td></td><td>3</td><td>-72.260</td><td>149.226</td><td>0</td><td>0.771</td></tr><tr><td>2</td><td>0</td><td></td><td></td><td>0</td><td></td><td></td><td></td><td></td><td>4</td><td>-73.132</td><td>151.652</td><td>2.426</td><td>0.229</td></tr></tbody></table>

## Parasitoid Abudance

<table><tbody><tr><td rowspan="10">100 m</td><td rowspan="5">model 1</td><td>1</td><td>0</td><td>NA</td><td>NA</td><td></td><td>NA</td><td></td><td></td><td>3</td><td>-71.737</td><td>148.268</td><td>0</td><td>0.568</td></tr><tr><td>2</td><td>0</td><td>-0.086</td><td>NA</td><td></td><td>NA</td><td></td><td></td><td>4</td><td>-72.447</td><td>150.425</td><td>2.157</td><td>0.193</td></tr><tr><td>3</td><td>0</td><td>NA</td><td>0.023</td><td></td><td>NA</td><td></td><td></td><td>4</td><td>-72.557</td><td>150.674</td><td>2.407</td><td>0.171</td></tr><tr><td>4</td><td>0</td><td>-0.088</td><td>-0.005</td><td></td><td>NA</td><td></td><td></td><td>5</td><td>-73.194</td><td>153.009</td><td>4.741</td><td>0.053</td></tr><tr><td>8</td><td>-0.054</td><td>-0.168</td><td>-0.056</td><td>-0.171</td><td></td><td></td><td></td><td>6</td><td>-73.075</td><td>155.555</td><td>7.287</td><td>0.015</td></tr><tr><td rowspan="5">model 2</td><td>1</td><td>0</td><td></td><td>NA</td><td>NA</td><td></td><td>NA</td><td></td><td>3</td><td>-71.737</td><td>148.268</td><td>0</td><td>0.538</td></tr><tr><td>3</td><td>0</td><td></td><td>NA</td><td>-0.134</td><td></td><td>NA</td><td></td><td>4</td><td>-72.275</td><td>150.035</td><td>1.767</td><td>0.222</td></tr><tr><td>2</td><td>0</td><td></td><td>0.023</td><td>NA</td><td></td><td>NA</td><td></td><td>4</td><td>-72.557</td><td>150.674</td><td>2.407</td><td>0.162</td></tr><tr><td>4</td><td>0</td><td></td><td>0.031</td><td>-0.136</td><td></td><td>NA</td><td></td><td>5</td><td>-73.072</td><td>152.584</td><td>4.316</td><td>0.062</td></tr><tr><td>8</td><td>0.001</td><td></td><td>0.037</td><td>-0.139</td><td></td><td>-0.022</td><td></td><td>6</td><td>-73.312</td><td>155.340</td><td>7.072</td><td>0.016</td></tr><tr><td rowspan="6">250 m</td><td rowspan="5">model 1</td><td>3</td><td>0</td><td>NA</td><td>0.278</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>4</td><td>-71.224</td><td>147.655</td><td>0</td><td>0.171</td></tr><tr><td>1</td><td>0</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>3</td><td>-71.737</td><td>148.268</td><td>0.613</td><td>0.126</td></tr><tr><td>4</td><td>0</td><td>0.201</td><td>0.354</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>5</td><td>-71.390</td><td>148.684</td><td>1.030</td><td>0.102</td></tr><tr><td>24</td><td>0.063</td><td>0.241</td><td>0.355</td><td>-0.474</td><td>NA</td><td>-0.302</td><td>NA</td><td>7</td><td>-70.938</td><td>148.957</td><td>1.303</td><td>0.089</td></tr><tr><td>7</td><td>0</td><td>NA</td><td>0.269</td><td>-0.166</td><td>NA</td><td>NA</td><td>NA</td><td>5</td><td>-71.600</td><td>149.007</td><td>1.353</td><td>0.087</td></tr><tr><td>8</td><td>0</td><td>0.249</td><td>0.362</td><td>-0.215</td><td>NA</td><td>NA</td><td>NA</td><td>6</td><td>-71.413</td><td>149.230</td><td>1.575</td><td>0.078</td></tr></tbody></table>
<table><thead><tr><th></th><th></th><th>(Intercept)</th><th>CD</th><th>MFS</th><th>SNH</th><th>CD: MFS</th><th>CD: SNH</th><th>MFS:SNH</th><th>CD:MFS: SNH</th><th>df</th><th>logLik</th><th>AICc</th><th>delta</th><th>weight</th></tr></thead><tbody><tr><td></td><td>5</td><td>0</td><td>NA</td><td>NA</td><td>-0.179</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>4</td><td>-72.034</td><td>149.490</td><td>1.836</td><td>0.068</td></tr><tr><td></td><td>56</td><td>0.049</td><td>0.274</td><td>0.451</td><td>-0.420</td><td>NA</td><td>-0.299</td><td>-0.277</td><td>NA</td><td>8</td><td>-70.791</td><td>150.342</td><td>2.688</td><td>0.045</td></tr><tr><td></td><td>2</td><td>0</td><td>0.066</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>4</td><td>-72.495</td><td>150.534</td><td>2.879</td><td>0.040</td></tr><tr><td></td><td>40</td><td>-0.014</td><td>0.283</td><td>0.459</td><td>-0.161</td><td>NA</td><td>NA</td><td>-0.282</td><td>NA</td><td>7</td><td>-71.256</td><td>150.665</td><td>3.010</td><td>0.038</td></tr><tr><td></td><td>39</td><td>-0.010</td><td>NA</td><td>0.331</td><td>-0.123</td><td>NA</td><td>NA</td><td>-0.203</td><td>NA</td><td>6</td><td>-71.700</td><td>151.083</td><td>3.429</td><td>0.031</td></tr><tr><td></td><td>12</td><td>-0.040</td><td>0.157</td><td>0.301</td><td>NA</td><td>-0.108</td><td>NA</td><td>NA</td><td>NA</td><td>6</td><td>-71.284</td><td>151.367</td><td>3.712</td><td>0.027</td></tr><tr><td></td><td>6</td><td>0</td><td>0.109</td><td>NA</td><td>-0.203</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>5</td><td>-72.656</td><td>151.626</td><td>3.971</td><td>0.023</td></tr><tr><td></td><td>22</td><td>0.065</td><td>0.103</td><td>NA</td><td>-0.473</td><td>NA</td><td>-0.313</td><td>NA</td><td>NA</td><td>6</td><td>-72.243</td><td>151.861</td><td>4.206</td><td>0.021</td></tr><tr><td></td><td>32</td><td>0.133</td><td>0.317</td><td>0.442</td><td>-0.507</td><td>0.178</td><td>-0.320</td><td>NA</td><td>NA</td><td>8</td><td>-70.799</td><td>151.872</td><td>4.218</td><td>0.021</td></tr><tr><td></td><td>16</td><td>0.012</td><td>0.263</td><td>0.377</td><td>-0.218</td><td>0.032</td><td>NA</td><td>NA</td><td>NA</td><td>7</td><td>-71.329</td><td>152.182</td><td>4.527</td><td>0.018</td></tr><tr><td></td><td>64</td><td>0.083</td><td>0.309</td><td>0.489</td><td>-0.438</td><td>0.086</td><td>-0.308</td><td>-0.266</td><td>NA</td><td>9</td><td>-70.703</td><td>153.712</td><td>6.058</td><td>0.008</td></tr><tr><td></td><td>48</td><td>-0.037</td><td>0.257</td><td>0.431</td><td>-0.154</td><td>-0.062</td><td>NA</td><td>-0.290</td><td>NA</td><td>8</td><td>-71.146</td><td>153.817</td><td>6.163</td><td>0.008</td></tr><tr><td></td><td>128</td><td>0.063</td><td>0.303</td><td>0.489</td><td>-0.363</td><td>0.061</td><td>-0.267</td><td>-0.068</td><td>0.415</td><td>10</td><td>-69.550</td><td>157.223</td><td>9.568</td><td>0.001</td></tr><tr><td rowspan="10">500 m</td><td rowspan="5">model 1</td><td>8</td><td>0.017</td><td>-0.495</td><td></td><td>0.103</td><td></td><td>0.885</td><td></td><td></td><td>6</td><td>-69.704</td><td>146.618</td><td>0</td><td>0.512</td></tr><tr><td>1</td><td>0</td><td>NA</td><td></td><td>NA</td><td></td><td>NA</td><td></td><td></td><td>3</td><td>-71.737</td><td>148.268</td><td>1.650</td><td>0.224</td></tr><tr><td>2</td><td>0</td><td>-0.206</td><td></td><td>NA</td><td></td><td>NA</td><td></td><td></td><td>4</td><td>-71.854</td><td>149.081</td><td>2.464</td><td>0.149</td></tr><tr><td>3</td><td>0</td><td>NA</td><td></td><td>-0.054</td><td></td><td>NA</td><td></td><td></td><td>4</td><td>-72.519</td><td>150.589</td><td>3.972</td><td>0.070</td></tr><tr><td>4</td><td>0</td><td>-0.208</td><td></td><td>-0.058</td><td></td><td>NA</td><td></td><td></td><td>5</td><td>-72.641</td><td>151.534</td><td>4.916</td><td>0.044</td></tr><tr><td rowspan="5">model 2</td><td>1</td><td>0</td><td>NA</td><td>NA</td><td></td><td></td><td>NA</td><td></td><td></td><td>3</td><td>-71.737</td><td>148.268</td><td>0</td><td>0.429</td></tr><tr><td>2</td><td>0</td><td>NA</td><td>NA</td><td></td><td></td><td>NA</td><td></td><td></td><td>4</td><td>-71.739</td><td>148.821</td><td>0.553</td><td>0.325</td></tr><tr><td>3</td><td>0</td><td>NA</td><td>NA</td><td>-0.054</td><td></td><td>NA</td><td></td><td></td><td>4</td><td>-72.519</td><td>150.590</td><td>2.322</td><td>0.134</td></tr><tr><td>4</td><td>0</td><td>NA</td><td>NA</td><td>0.015</td><td></td><td>NA</td><td></td><td></td><td>5</td><td>-72.537</td><td>151.398</td><td>3.130</td><td>0.090</td></tr><tr><td>8</td><td>0.012</td><td>NA</td><td>NA</td><td>-0.001</td><td></td><td>0.041</td><td></td><td></td><td>6</td><td>-72.561</td><td>154.145</td><td>5.878</td><td>0.023</td></tr></tbody></table>
<table><thead><tr><th></th><th></th><th></th><th>(Intercept)</th><th>CD</th><th>MFS</th><th>SNH</th><th>CD: MFS</th><th>CD: SNH</th><th>MFS:SNH</th><th>CD:MFS: SNH</th><th>df</th><th>logLik</th><th>AICc</th><th>delta</th><th>weight</th></tr></thead><tbody><tr><td rowspan="5">1000 m</td><td rowspan="5">model 1</td><td>2</td><td>0</td><td>-0.266</td><td>NA</td><td>NA</td><td>NA</td><td></td><td></td><td></td><td></td><td>4</td><td>-71.344</td><td>147.926</td><td>0</td><td>0.383</td></tr><tr><td>1</td><td>0</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td></td><td></td><td></td><td></td><td>3</td><td>-71.737</td><td>148.268</td><td>0.342</td><td>0.322</td></tr><tr><td>4</td><td>0</td><td>-0.266</td><td>0.030</td><td>NA</td><td></td><td></td><td></td><td></td><td></td><td>5</td><td>-72.204</td><td>150.473</td><td>2.546</td><td>0.107</td></tr><tr><td>3</td><td>0</td><td>NA</td><td>0.027</td><td>NA</td><td></td><td></td><td></td><td></td><td></td><td>4</td><td>-72.554</td><td>150.667</td><td>2.741</td><td>0.097</td></tr><tr><td>8</td><td>0.002</td><td>-0.285</td><td>0.072</td><td>-0.187</td><td></td><td></td><td></td><td></td><td></td><td>6</td><td>-72.323</td><td>150.802</td><td>2.876</td><td>0.091</td></tr><tr><td rowspan="2">model 2</td><td>1</td><td>0</td><td></td><td></td><td>NA</td><td></td><td></td><td></td><td></td><td></td><td>3</td><td>-71.737</td><td>148.268</td><td>0</td><td>0.770</td></tr><tr><td>2</td><td>0</td><td></td><td></td><td>-0.015</td><td></td><td></td><td></td><td></td><td></td><td>4</td><td>-72.562</td><td>150.685</td><td>2.418</td><td>0.230</td></tr></tbody></table>

## Herbivore

### Shannon

<table><tbody><tr><td rowspan="5">100 m</td><td rowspan="5">model 1</td><td>1</td><td>0</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td></td><td></td><td></td><td></td><td>3</td><td>-71.633</td><td>148.074</td><td>0</td><td>0.471</td></tr><tr><td>2</td><td>0</td><td>0.189</td><td>NA</td><td>NA</td><td>NA</td><td></td><td></td><td></td><td></td><td>4</td><td>-71.873</td><td>149.183</td><td>1.109</td><td>0.271</td></tr><tr><td>3</td><td>0</td><td>NA</td><td>-0.034</td><td>NA</td><td></td><td></td><td></td><td></td><td></td><td>4</td><td>-72.436</td><td>150.459</td><td>2.385</td><td>0.143</td></tr><tr><td>4</td><td>0</td><td>0.198</td><td>0.030</td><td>NA</td><td></td><td></td><td></td><td></td><td></td><td>5</td><td>-72.631</td><td>151.736</td><td>3.662</td><td>0.076</td></tr><tr><td>8</td><td>-0.134</td><td>-0.002</td><td>-0.097</td><td>-0.424</td><td></td><td></td><td></td><td></td><td></td><td>6</td><td>-72.065</td><td>153.047</td><td>4.974</td><td>0.039</td></tr><tr><td rowspan="5">model 2</td><td>1</td><td>0</td><td></td><td>NA</td><td>NA</td><td></td><td></td><td></td><td></td><td></td><td>3</td><td>-71.633</td><td>148.074</td><td>0</td><td>0.558</td></tr><tr><td>3</td><td>0</td><td></td><td>NA</td><td>0.093</td><td></td><td></td><td></td><td></td><td></td><td>4</td><td>-72.317</td><td>150.188</td><td>2.114</td><td>0.194</td></tr><tr><td>2</td><td>0</td><td></td><td>-0.034</td><td>NA</td><td></td><td></td><td></td><td></td><td></td><td>4</td><td>-72.436</td><td>150.459</td><td>2.385</td><td>0.169</td></tr><tr><td>4</td><td>0</td><td></td><td>-0.039</td><td>0.095</td><td></td><td></td><td></td><td></td><td></td><td>5</td><td>-73.088</td><td>152.717</td><td>4.643</td><td>0.055</td></tr><tr><td>8</td><td>0.017</td><td></td><td>0.033</td><td>0.049</td><td></td><td></td><td>-0.286</td><td></td><td></td><td>6</td><td>-72.913</td><td>154.430</td><td>6.356</td><td>0.023</td></tr><tr><td rowspan="6">250 m</td><td rowspan="6">model 1</td><td>1</td><td>0</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td></td><td>3</td><td>-71.633</td><td>148.074</td><td>0</td><td>0.182</td></tr><tr><td>2</td><td>0</td><td>0.247</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td></td><td>4</td><td>-71.426</td><td>148.170</td><td>0.096</td><td>0.174</td></tr><tr><td>5</td><td>0</td><td>NA</td><td>NA</td><td>0.238</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td></td><td>4</td><td>-71.509</td><td>148.358</td><td>0.284</td><td>0.158</td></tr><tr><td>6</td><td>0</td><td>0.206</td><td>NA</td><td>0.194</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td></td><td>5</td><td>-71.628</td><td>149.222</td><td>1.148</td><td>0.103</td></tr><tr><td>3</td><td>0</td><td>NA</td><td>-0.086</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td></td><td>4</td><td>-72.337</td><td>150.233</td><td>2.160</td><td>0.062</td></tr><tr><td>7</td><td>0</td><td>NA</td><td>-0.075</td><td>0.234</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td></td><td>5</td><td>-72.265</td><td>150.717</td><td>2.644</td><td>0.049</td></tr></tbody></table>
<table><thead><tr><th></th><th></th><th></th><th>(Intercept)</th><th>CD</th><th>MFS</th><th>SNH</th><th>CD: MFS</th><th>CD: SNH</th><th>MFS:SNH</th><th>CD:MFS: SNH</th><th>df</th><th>logLik</th><th>AICc</th><th>delta</th><th>weight</th></tr></thead><tbody><tr><td rowspan="12">500 m</td><td rowspan="12">model 1</td><td>4</td><td>0</td><td>0.251</td><td>0.009</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>5</td><td>-72.203</td><td>150.752</td><td>2.678</td><td>0.048</td></tr><tr><td>39</td><td>0.019</td><td>NA</td><td>-0.196</td><td>0.149</td><td>NA</td><td>NA</td><td>0.402</td><td>NA</td><td>6</td><td>-71.609</td><td>150.981</td><td>2.908</td><td>0.043</td></tr><tr><td>22</td><td>0.036</td><td>0.203</td><td>NA</td><td>0.045</td><td>NA</td><td>-0.173</td><td>NA</td><td>NA</td><td>6</td><td>-71.924</td><td>151.162</td><td>3.089</td><td>0.039</td></tr><tr><td>16</td><td>-0.266</td><td>-0.100</td><td>-0.349</td><td>0.263</td><td>-0.715</td><td>NA</td><td>NA</td><td>NA</td><td>7</td><td>-71.043</td><td>151.553</td><td>3.479</td><td>0.032</td></tr><tr><td>12</td><td>-0.203</td><td>0.029</td><td>-0.257</td><td>NA</td><td>-0.546</td><td>NA</td><td>NA</td><td>NA</td><td>6</td><td>-71.348</td><td>151.670</td><td>3.597</td><td>0.030</td></tr><tr><td>8</td><td>0</td><td>0.207</td><td>0.002</td><td>0.194</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>6</td><td>-72.415</td><td>151.984</td><td>3.910</td><td>0.026</td></tr><tr><td>40</td><td>0.017</td><td>0.164</td><td>-0.121</td><td>0.126</td><td>NA</td><td>NA</td><td>0.356</td><td>NA</td><td>7</td><td>-71.982</td><td>152.902</td><td>4.828</td><td>0.016</td></tr><tr><td>48</td><td>-0.219</td><td>-0.094</td><td>-0.400</td><td>0.202</td><td>-0.625</td><td>NA</td><td>0.275</td><td>NA</td><td>8</td><td>-70.913</td><td>153.332</td><td>5.259</td><td>0.013</td></tr><tr><td>24</td><td>0.036</td><td>0.202</td><td>-0.002</td><td>0.045</td><td>NA</td><td>-0.173</td><td>NA</td><td>NA</td><td>7</td><td>-72.696</td><td>154.122</td><td>6.048</td><td>0.009</td></tr><tr><td>32</td><td>-0.226</td><td>-0.082</td><td>-0.328</td><td>0.168</td><td>-0.667</td><td>-0.105</td><td>NA</td><td>NA</td><td>8</td><td>-71.537</td><td>154.390</td><td>6.317</td><td>0.008</td></tr><tr><td>56</td><td>0.054</td><td>0.159</td><td>-0.126</td><td>-0.026</td><td>NA</td><td>-0.176</td><td>0.359</td><td>NA</td><td>8</td><td>-72.260</td><td>155.120</td><td>7.046</td><td>0.005</td></tr><tr><td>64</td><td>-0.173</td><td>-0.075</td><td>-0.378</td><td>0.094</td><td>-0.569</td><td>-0.117</td><td>0.284</td><td>NA</td><td>9</td><td>-71.374</td><td>156.298</td><td>8.225</td><td>0.003</td></tr><tr><td>128</td><td>-0.192</td><td>-0.081</td><td>-0.378</td><td>0.160</td><td>-0.591</td><td>-0.081</td><td>0.460</td><td>0.367</td><td>10</td><td>-70.152</td><td>159.884</td><td>11.811</td><td>0</td></tr><tr><td rowspan="12">model 2</td><td>1</td><td>0</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>3</td><td>-71.633</td><td>148.074</td><td>0</td><td>0.368</td></tr><tr><td>3</td><td>0</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>4</td><td>-71.502</td><td>148.342</td><td>0.269</td><td>0.322</td></tr><tr><td>2</td><td>0</td><td>0.128</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>4</td><td>-72.192</td><td>149.906</td><td>1.832</td><td>0.147</td></tr><tr><td>4</td><td>0</td><td>0.133</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>5</td><td>-72.054</td><td>150.201</td><td>2.127</td><td>0.127</td></tr><tr><td>8</td><td>0.003</td><td>0.075</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>6</td><td>-71.977</td><td>152.696</td><td>4.622</td><td>0.036</td></tr><tr><td>1</td><td>0</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>3</td><td>-71.633</td><td>148.074</td><td>0</td><td>0.380</td></tr><tr><td>3</td><td>0</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>4</td><td>-71.502</td><td>148.342</td><td>0.269</td><td>0.333</td></tr><tr><td>2</td><td>0</td><td>-0.137</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>4</td><td>-72.152</td><td>149.815</td><td>1.741</td><td>0.159</td></tr><tr><td>4</td><td>0</td><td>-0.072</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>5</td><td>-72.227</td><td>150.738</td><td>2.664</td><td>0.100</td></tr><tr><td>8</td><td>-0.040</td><td>-0.043</td><td>NA</td><td>NA</td><td>NA</td><td>-0.135</td><td>NA</td><td>NA</td><td>6</td><td>-72.204</td><td>153.345</td><td>5.271</td><td>0.027</td></tr><tr><td rowspan="2">1000 m</td><td rowspan="2">model 1</td><td>3</td><td>0</td><td>NA</td><td>-0.435</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>4</td><td>-68.680</td><td>141.946</td><td>0</td><td>0.602</td></tr><tr><td>4</td><td>0</td><td>0.135</td><td>-0.436</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>5</td><td>-69.266</td><td>143.430</td><td>1.484</td><td>0.287</td></tr></tbody></table>
<table><thead><tr><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th></tr><tr><th></th><th></th><th>(Intercept)</th><th>CD</th><th>MFS</th><th>SNH</th><th>CD: MFS</th><th>CD: SNH</th><th>MFS:SNH</th><th>CD:MFS: SNH</th><th>df</th><th>logLik</th><th>AICc</th><th>delta</th><th>weight</th></tr></thead><tbody><tr><td></td><td>8</td><td>0</td><td>0.134</td><td>-0.435</td><td></td><td>-0.005</td><td></td><td></td><td></td><td>6</td><td>-70.509</td><td>146.190</td><td>4.244</td><td>0.072</td></tr><tr><td></td><td>1</td><td>0</td><td>NA</td><td>NA</td><td></td><td>NA</td><td></td><td></td><td></td><td>3</td><td>-71.633</td><td>148.074</td><td>6.128</td><td>0.028</td></tr><tr><td></td><td>2</td><td>0</td><td>0.129</td><td>NA</td><td></td><td>NA</td><td></td><td></td><td></td><td>4</td><td>-72.187</td><td>149.894</td><td>7.947</td><td>0.011</td></tr><tr><td rowspan="2">model 2</td><td>1</td><td>0</td><td></td><td></td><td>NA</td><td></td><td></td><td></td><td></td><td>3</td><td>-71.633</td><td>148.074</td><td>0</td><td>0.699</td></tr><tr><td>2</td><td>0</td><td></td><td></td><td>0.143</td><td></td><td></td><td></td><td></td><td>4</td><td>-72.127</td><td>149.758</td><td>1.684</td><td>0.301</td></tr><tr><td colspan="21" style="text-align: left; font-weight: bold;">Predator</td></tr><tr><td colspan="21" style="text-align: left; font-weight: bold;">Shannon</td></tr><tr><td rowspan="6">100 m</td><td>model 1</td><td>1</td><td>0</td><td>NA</td><td>NA</td><td></td><td>NA</td><td></td><td></td><td>3</td><td>-71.981</td><td>148.718</td><td>0</td><td>0.573</td></tr><tr><td></td><td>2</td><td>0</td><td>-0.054</td><td>NA</td><td></td><td>NA</td><td></td><td></td><td>4</td><td>-72.778</td><td>151.033</td><td>2.315</td><td>0.180</td></tr><tr><td></td><td>3</td><td>0</td><td>NA</td><td>-0.011</td><td></td><td>NA</td><td></td><td></td><td>4</td><td>-72.825</td><td>151.139</td><td>2.421</td><td>0.171</td></tr><tr><td></td><td>4</td><td>0</td><td>-0.064</td><td>-0.032</td><td></td><td>NA</td><td></td><td></td><td>5</td><td>-73.526</td><td>153.584</td><td>4.866</td><td>0.050</td></tr><tr><td></td><td>8</td><td>-0.136</td><td>-0.268</td><td>-0.161</td><td></td><td>-0.431</td><td></td><td></td><td>6</td><td>-72.943</td><td>154.879</td><td>6.161</td><td>0.026</td></tr><tr><td>model 2</td><td>1</td><td>0</td><td></td><td>NA</td><td>NA</td><td></td><td></td><td>NA</td><td>3</td><td>-71.981</td><td>148.718</td><td>0</td><td>0.433</td></tr><tr><td></td><td>3</td><td>0</td><td></td><td>NA</td><td>-0.149</td><td></td><td></td><td>NA</td><td>4</td><td>-72.451</td><td>150.291</td><td>1.573</td><td>0.197</td></tr><tr><td></td><td>8</td><td>0.034</td><td></td><td>0.144</td><td>-0.244</td><td></td><td></td><td>-0.579</td><td>6</td><td>-71.549</td><td>150.398</td><td>1.680</td><td>0.187</td></tr><tr><td></td><td>2</td><td>0</td><td></td><td>-0.011</td><td>NA</td><td></td><td></td><td>NA</td><td>4</td><td>-72.825</td><td>151.139</td><td>2.421</td><td>0.129</td></tr><tr><td></td><td>4</td><td>0</td><td></td><td>-0.002</td><td>-0.149</td><td></td><td></td><td>NA</td><td>5</td><td>-73.287</td><td>152.875</td><td>4.157</td><td>0.054</td></tr><tr><td rowspan="6">250 m</td><td>model 1</td><td>1</td><td>0</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>3</td><td>-71.981</td><td>148.718</td><td>0</td><td>0.229</td></tr><tr><td></td><td>5</td><td>0</td><td>NA</td><td>NA</td><td>-0.217</td><td>NA</td><td>NA</td><td>NA</td><td>4</td><td>-72.008</td><td>149.287</td><td>0.569</td><td>0.173</td></tr><tr><td></td><td>2</td><td>0</td><td>-0.175</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>4</td><td>-72.301</td><td>149.951</td><td>1.233</td><td>0.124</td></tr><tr><td></td><td>3</td><td>0</td><td>NA</td><td>0.143</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>4</td><td>-72.483</td><td>150.364</td><td>1.646</td><td>0.101</td></tr><tr><td></td><td>6</td><td>0</td><td>-0.135</td><td>NA</td><td>-0.188</td><td>NA</td><td>NA</td><td>NA</td><td>5</td><td>-72.545</td><td>151.125</td><td>2.407</td><td>0.069</td></tr><tr><td></td><td>7</td><td>0</td><td>NA</td><td>0.132</td><td>-0.210</td><td>NA</td><td>NA</td><td>NA</td><td>5</td><td>-72.567</td><td>151.126</td><td>2.408</td><td>0.069</td></tr><tr><td></td><td>39</td><td>-0.018</td><td>NA</td><td>0.248</td><td>-0.128</td><td>NA</td><td>NA</td><td>-0.384</td><td>6</td><td>-71.971</td><td>151.462</td><td>2.744</td><td>0.058</td></tr></tbody></table>
<table><thead><tr><th></th><th></th><th></th><th>(Intercept)</th><th>CD</th><th>MFS</th><th>SNH</th><th>CD: MFS</th><th>CD: SNH</th><th>MFS:SNH</th><th>CD:MFS: SNH</th><th>df</th><th>logLik</th><th>AICc</th><th>delta</th><th>weight</th></tr></thead><tbody><tr><td rowspan="14">500 m</td><td rowspan="14">model 1</td><td>4</td><td>0</td><td>-0.142</td><td>0.089</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>5</td><td>-72.958</td><td>152.262</td><td>3.544</td><td>0.039</td></tr><tr><td>12</td><td>-0.230</td><td>-0.393</td><td>-0.212</td><td>NA</td><td>-0.618</td><td>NA</td><td>NA</td><td>NA</td><td>6</td><td>-71.889</td><td>152.619</td><td>3.901</td><td>0.033</td></tr><tr><td>8</td><td>0</td><td>-0.098</td><td>0.096</td><td>-0.191</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>6</td><td>-73.187</td><td>153.539</td><td>4.821</td><td>0.021</td></tr><tr><td>22</td><td>-0.014</td><td>-0.134</td><td>NA</td><td>-0.131</td><td>NA</td><td>0.066</td><td>NA</td><td>NA</td><td>6</td><td>-73.099</td><td>153.771</td><td>5.053</td><td>0.018</td></tr><tr><td>48</td><td>-0.274</td><td>-0.334</td><td>-0.079</td><td>-0.039</td><td>-0.677</td><td>NA</td><td>-0.456</td><td>NA</td><td>8</td><td>-71.457</td><td>154.191</td><td>5.473</td><td>0.015</td></tr><tr><td>40</td><td>-0.018</td><td>-0.054</td><td>0.223</td><td>-0.121</td><td>NA</td><td>NA</td><td>-0.369</td><td>NA</td><td>7</td><td>-72.699</td><td>154.305</td><td>5.587</td><td>0.014</td></tr><tr><td>128</td><td>-0.228</td><td>-0.325</td><td>-0.111</td><td>-0.279</td><td>-0.620</td><td>-0.059</td><td>-1.476</td><td>-2.109</td><td>10</td><td>-69.128</td><td>154.675</td><td>5.957</td><td>0.012</td></tr><tr><td>16</td><td>-0.197</td><td>-0.325</td><td>-0.163</td><td>-0.140</td><td>-0.529</td><td>NA</td><td>NA</td><td>NA</td><td>7</td><td>-72.387</td><td>154.734</td><td>6.016</td><td>0.011</td></tr><tr><td>24</td><td>-0.015</td><td>-0.096</td><td>0.098</td><td>-0.131</td><td>NA</td><td>0.070</td><td>NA</td><td>NA</td><td>7</td><td>-73.712</td><td>156.368</td><td>7.650</td><td>0.005</td></tr><tr><td>64</td><td>-0.333</td><td>-0.359</td><td>-0.107</td><td>0.100</td><td>-0.749</td><td>0.151</td><td>-0.468</td><td>NA</td><td>9</td><td>-71.836</td><td>156.811</td><td>8.093</td><td>0.004</td></tr><tr><td>56</td><td>-0.033</td><td>-0.052</td><td>0.225</td><td>-0.058</td><td>NA</td><td>0.073</td><td>-0.370</td><td>NA</td><td>8</td><td>-73.240</td><td>157.325</td><td>8.607</td><td>0.003</td></tr><tr><td>32</td><td>-0.246</td><td>-0.347</td><td>-0.190</td><td>-0.022</td><td>-0.588</td><td>0.130</td><td>NA</td><td>NA</td><td>8</td><td>-72.787</td><td>157.431</td><td>8.713</td><td>0.003</td></tr><tr><td rowspan="14">model 2</td><td>1</td><td>0</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>3</td><td>-71.981</td><td>148.718</td><td>0</td><td>0.462</td></tr><tr><td>3</td><td>0</td><td>NA</td><td>-0.196</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>4</td><td>-72.163</td><td>149.638</td><td>0.920</td><td>0.291</td></tr><tr><td>2</td><td>0</td><td>0.045</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>4</td><td>-72.794</td><td>151.069</td><td>2.351</td><td>0.143</td></tr><tr><td>4</td><td>0</td><td>0.041</td><td>-0.195</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>5</td><td>-72.992</td><td>152.155</td><td>3.437</td><td>0.083</td></tr><tr><td>8</td><td>0.002</td><td>0.010</td><td>-0.178</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>6</td><td>-72.976</td><td>154.843</td><td>6.125</td><td>0.022</td></tr><tr><td>1</td><td>0</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>3</td><td>-71.981</td><td>148.718</td><td>0</td><td>0.363</td></tr><tr><td>2</td><td>0</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>4</td><td>-72.026</td><td>149.328</td><td>0.610</td><td>0.267</td></tr><tr><td>3</td><td>0</td><td>NA</td><td>-0.196</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>4</td><td>-72.163</td><td>149.638</td><td>0.920</td><td>0.229</td></tr><tr><td>4</td><td>0</td><td>NA</td><td>-0.144</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>5</td><td>-72.511</td><td>151.105</td><td>2.387</td><td>0.110</td></tr><tr><td>8</td><td>-0.047</td><td>NA</td><td>NA</td><td>-0.084</td><td>NA</td><td>NA</td><td>-0.158</td><td>NA</td><td>6</td><td>-72.493</td><td>153.638</td><td>4.920</td><td>0.031</td></tr><tr><td rowspan="3">1000 m</td><td rowspan="3">model 1</td><td>3</td><td>0</td><td>NA</td><td>0.299</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>4</td><td>-71.182</td><td>147.415</td><td>0</td><td>0.494</td></tr><tr><td>1</td><td>0</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>3</td><td>-71.981</td><td>148.718</td><td>1.303</td><td>0.258</td></tr><tr><td>4</td><td>0</td><td>-0.006</td><td>0.299</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>5</td><td>-72.104</td><td>149.999</td><td>2.583</td><td>0.136</td></tr></tbody></table>
<table><thead><tr><th></th><th></th><th>(Intercept)</th><th>CD</th><th>MFS</th><th>SNH</th><th>CD: MFS</th><th>CD: SNH</th><th>MFS:SNH</th><th>CD:MFS: SNH</th><th>df</th><th>logLik</th><th>AICc</th><th>delta</th><th>weight</th></tr></thead><tbody><tr><td></td><td>2</td><td>0</td><td>-0.003</td><td>NA</td><td></td><td>NA</td><td></td><td></td><td></td><td>4</td><td>-72.827</td><td>151.143</td><td>3.728</td><td>0.077</td></tr><tr><td></td><td>8</td><td>0</td><td>-0.010</td><td>0.307</td><td></td><td>-0.036</td><td></td><td></td><td></td><td>6</td><td>-73.163</td><td>152.667</td><td>5.252</td><td>0.036</td></tr><tr><td rowspan="2">model 2</td><td>1</td><td>0</td><td></td><td></td><td>NA</td><td></td><td></td><td></td><td></td><td>3</td><td>-71.981</td><td>148.718</td><td>0</td><td>0.633</td></tr><tr><td>2</td><td>0</td><td></td><td></td><td>-0.185</td><td></td><td></td><td></td><td></td><td>4</td><td>-72.239</td><td>149.811</td><td>1.093</td><td>0.367</td></tr></tbody></table>

## Parasitoid Shannon

<table><tbody><tr><td rowspan="10">100 m</td><td rowspan="5">model 1</td><td>1</td><td>0</td><td>NA</td><td>NA</td><td>NA</td><td></td><td></td><td></td><td>3</td><td>-72.592</td><td>149.807</td><td>0</td><td>0.538</td></tr><tr><td>2</td><td>0</td><td>0.088</td><td>NA</td><td>NA</td><td></td><td></td><td></td><td>4</td><td>-73.359</td><td>151.902</td><td>2.095</td><td>0.189</td></tr><tr><td>3</td><td>0</td><td>NA</td><td>0.060</td><td>NA</td><td></td><td></td><td></td><td>4</td><td>-73.436</td><td>152.077</td><td>2.270</td><td>0.173</td></tr><tr><td>4</td><td>0</td><td>0.119</td><td>0.099</td><td>NA</td><td></td><td></td><td></td><td>5</td><td>-74.035</td><td>154.102</td><td>4.296</td><td>0.063</td></tr><tr><td>8</td><td>-0.136</td><td>-0.083</td><td>-0.030</td><td>-0.429</td><td></td><td></td><td></td><td>6</td><td>-73.434</td><td>155.131</td><td>5.325</td><td>0.038</td></tr><tr><td rowspan="5">model 2</td><td>1</td><td>0</td><td></td><td>NA</td><td>NA</td><td></td><td>NA</td><td></td><td>3</td><td>-72.592</td><td>149.807</td><td>0</td><td>0.472</td></tr><tr><td>3</td><td>0</td><td></td><td>NA</td><td>0.130</td><td></td><td>NA</td><td></td><td>4</td><td>-73.180</td><td>151.497</td><td>1.690</td><td>0.203</td></tr><tr><td>2</td><td>0</td><td></td><td>0.060</td><td>NA</td><td></td><td>NA</td><td></td><td>4</td><td>-73.436</td><td>152.077</td><td>2.270</td><td>0.152</td></tr><tr><td>8</td><td>0.028</td><td></td><td>0.175</td><td>0.047</td><td></td><td>-0.486</td><td></td><td>6</td><td>-72.798</td><td>152.634</td><td>2.827</td><td>0.115</td></tr><tr><td>4</td><td>0</td><td></td><td>0.053</td><td>0.127</td><td></td><td>NA</td><td></td><td>5</td><td>-74.029</td><td>153.959</td><td>4.152</td><td>0.059</td></tr></tbody></table>

<table><tbody><tr><td rowspan="12">250 m</td><td rowspan="12">model 1</td><td>1</td><td>0</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>3</td><td>-72.592</td><td>149.807</td><td>0</td><td>0.217</td></tr><tr><td>3</td><td>0</td><td>NA</td><td>0.211</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>4</td><td>-72.612</td><td>150.210</td><td>0.404</td><td>0.177</td></tr><tr><td>4</td><td>0</td><td>0.182</td><td>0.280</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>5</td><td>-72.889</td><td>151.375</td><td>1.568</td><td>0.099</td></tr><tr><td>5</td><td>0</td><td>NA</td><td>NA</td><td>0.108</td><td>NA</td><td>NA</td><td>NA</td><td>4</td><td>-73.282</td><td>151.728</td><td>1.922</td><td>0.083</td></tr><tr><td>2</td><td>0</td><td>0.076</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>4</td><td>-73.396</td><td>151.987</td><td>2.180</td><td>0.073</td></tr><tr><td>7</td><td>0</td><td>NA</td><td>0.217</td><td>0.118</td><td>NA</td><td>NA</td><td>NA</td><td>5</td><td>-73.267</td><td>152.108</td><td>2.301</td><td>0.069</td></tr><tr><td>24</td><td>0.073</td><td>0.152</td><td>0.269</td><td>-0.215</td><td>NA</td><td>-0.351</td><td>NA</td><td>7</td><td>-72.731</td><td>152.194</td><td>2.387</td><td>0.066</td></tr><tr><td>22</td><td>0.075</td><td>0.048</td><td>NA</td><td>-0.214</td><td>NA</td><td>-0.360</td><td>NA</td><td>6</td><td>-73.228</td><td>153.075</td><td>3.268</td><td>0.042</td></tr><tr><td>12</td><td>-0.114</td><td>0.057</td><td>0.131</td><td>NA</td><td>-0.307</td><td>NA</td><td>NA</td><td>6</td><td>-72.587</td><td>153.414</td><td>3.607</td><td>0.036</td></tr></tbody></table>
<table><thead><tr><th></th><th></th><th></th><th>(Intercept)</th><th>CD</th><th>MFS</th><th>SNH</th><th>CD: MFS</th><th>CD: SNH</th><th>MFS:SNH</th><th>CD:MFS: SNH</th><th>df</th><th>logLik</th><th>AICc</th><th>delta</th><th>weight</th></tr></thead><tbody><tr><td rowspan="12">500 m</td><td rowspan="6">model 1</td><td>8</td><td>0</td><td>0.162</td><td>0.277</td><td>0.087</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>6</td><td>-73.664</td><td>153.756</td><td>3.949</td><td>0.030</td></tr><tr><td>6</td><td>0</td><td>0.055</td><td>NA</td><td>0.096</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>5</td><td>-74.100</td><td>154.185</td><td>4.379</td><td>0.024</td></tr><tr><td>39</td><td>0.009</td><td>NA</td><td>0.163</td><td>0.081</td><td>NA</td><td>NA</td><td>0.178</td><td>NA</td><td>6</td><td>-73.451</td><td>154.302</td><td>4.495</td><td>0.023</td></tr><tr><td>32</td><td>-0.020</td><td>0.051</td><td>0.153</td><td>-0.172</td><td>-0.237</td><td>-0.327</td><td>NA</td><td>NA</td><td>8</td><td>-72.548</td><td>154.864</td><td>5.058</td><td>0.017</td></tr><tr><td>56</td><td>0.080</td><td>0.135</td><td>0.219</td><td>-0.244</td><td>NA</td><td>-0.353</td><td>0.143</td><td>NA</td><td>8</td><td>-73.042</td><td>154.886</td><td>5.079</td><td>0.017</td></tr><tr><td>16</td><td>-0.144</td><td>-0.004</td><td>0.087</td><td>0.124</td><td>-0.387</td><td>NA</td><td>NA</td><td>NA</td><td>7</td><td>-73.186</td><td>155.601</td><td>5.795</td><td>0.012</td></tr><tr><td rowspan="6">model 2</td><td>40</td><td>0.007</td><td>0.146</td><td>0.229</td><td>0.061</td><td>NA</td><td>NA</td><td>0.137</td><td>NA</td><td>7</td><td>-73.920</td><td>156.369</td><td>6.562</td><td>0.008</td></tr><tr><td>64</td><td>0.002</td><td>0.054</td><td>0.132</td><td>-0.202</td><td>-0.197</td><td>-0.332</td><td>0.117</td><td>NA</td><td>9</td><td>-72.869</td><td>157.977</td><td>8.170</td><td>0.004</td></tr><tr><td>48</td><td>-0.128</td><td>-0.002</td><td>0.070</td><td>0.104</td><td>-0.357</td><td>NA</td><td>0.091</td><td>NA</td><td>8</td><td>-73.484</td><td>158.628</td><td>8.821</td><td>0.003</td></tr><tr><td>128</td><td>0.002</td><td>0.054</td><td>0.132</td><td>-0.202</td><td>-0.197</td><td>-0.332</td><td>0.118</td><td>0.002</td><td>10</td><td>-71.778</td><td>161.705</td><td>11.899</td><td>0.001</td></tr><tr><td>1</td><td>0</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>3</td><td>-72.592</td><td>149.807</td><td>0</td><td>0.427</td></tr><tr><td>2</td><td>0</td><td>-0.181</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>4</td><td>-72.862</td><td>150.777</td><td>0.970</td><td>0.263</td></tr><tr><td rowspan="6">model 1</td><td>3</td><td>0</td><td>NA</td><td>0.125</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>4</td><td>-73.204</td><td>151.552</td><td>1.745</td><td>0.179</td></tr><tr><td>4</td><td>0</td><td>-0.178</td><td>0.121</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>5</td><td>-73.496</td><td>152.661</td><td>2.854</td><td>0.103</td></tr><tr><td>8</td><td>0.003</td><td>-0.225</td><td>0.148</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>6</td><td>-73.515</td><td>155.222</td><td>5.415</td><td>0.029</td></tr><tr><td>1</td><td>0</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>3</td><td>-72.592</td><td>149.807</td><td>0</td><td>0.470</td></tr><tr><td>2</td><td>0</td><td>0.125</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>4</td><td>-73.204</td><td>151.551</td><td>1.744</td><td>0.197</td></tr><tr><td>3</td><td>0</td><td>NA</td><td>0.125</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>4</td><td>-73.204</td><td>151.552</td><td>1.745</td><td>0.197</td></tr><tr><td rowspan="6">model 2</td><td>4</td><td>0</td><td>0.179</td><td>0.179</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>5</td><td>-73.499</td><td>152.785</td><td>2.978</td><td>0.106</td></tr><tr><td>8</td><td>0.049</td><td>0.144</td><td>0.117</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>6</td><td>-73.512</td><td>155.272</td><td>5.465</td><td>0.031</td></tr><tr><td>1</td><td>0</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>3</td><td>-72.592</td><td>149.807</td><td>0</td><td>0.352</td></tr><tr><td>2</td><td>0</td><td>-0.198</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>4</td><td>-72.728</td><td>150.472</td><td>0.666</td><td>0.252</td></tr><tr><td>3</td><td>0</td><td>NA</td><td>-0.176</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>4</td><td>-72.892</td><td>150.845</td><td>1.038</td><td>0.210</td></tr><tr><td>1000 m</td><td>model 1</td><td>1</td><td>0</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>3</td><td>-72.592</td><td>149.807</td><td>0</td><td>0.352</td></tr><tr><td rowspan="3">model 2</td><td>2</td><td>0</td><td>-0.198</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>4</td><td>-72.728</td><td>150.472</td><td>0.666</td><td>0.252</td></tr><tr><td>3</td><td>0</td><td>NA</td><td>-0.176</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>4</td><td>-72.892</td><td>150.845</td><td>1.038</td><td>0.210</td></tr></tbody></table>
<table><thead><tr><th></th><th>(Intercept)</th><th>CD</th><th>MFS</th><th>SNH</th><th>CD: MFS</th><th>CD: SNH</th><th>MFS:SNH</th><th>CD:MFS: SNH</th><th>df</th><th>logLik</th><th>AICc</th><th>delta</th><th>weight</th></tr></thead><tbody><tr><td></td><td>4</td><td>0</td><td>-0.196</td><td>-0.174</td><td>NA</td><td></td><td></td><td></td><td>5</td><td>-73.040</td><td>151.554</td><td>1.748</td><td>0.147</td></tr><tr><td></td><td>8</td><td>0</td><td>-0.192</td><td>-0.182</td><td>0.036</td><td></td><td></td><td></td><td>6</td><td>-74.148</td><td>154.212</td><td>4.405</td><td>0.039</td></tr><tr><td>model 2</td><td>1</td><td>0</td><td></td><td>NA</td><td></td><td></td><td></td><td></td><td>3</td><td>-72.592</td><td>149.807</td><td>0</td><td>0.752</td></tr><tr><td></td><td>2</td><td>0</td><td></td><td>0.069</td><td></td><td></td><td></td><td></td><td>4</td><td>-73.414</td><td>152.028</td><td>2.221</td><td>0.248</td></tr></tbody></table>
<caption>**Table S7. Model-averaged estimates with unconditional lower and upper 95% confidence intervals.**</caption>

Model-averaged estimates with unconditional lower (LC95) and upper (UC95) 95% confidence intervals for the abundance of herbivores, predators and parasitoids, and Shannon diversity of herbivores, predators and parasitoids as a function of landscape predictors at each spatial scale in alternative models 1 and 2 (see **Table S2**). Model averaging was based on a set of top models (ΔAICc<7). CD: crop diversity; MFS: mean field size; SNH: semi-natural habitat proportion. Estimates are highlighted in bold if 95% confidence intervals do not overlap with zero. Grey cells coincide with predictors not included to either model due to collinearity. The italicized row (model 2) corresponds to the common landscape predictor between model 2 and model 1.

<table><thead><tr><th>Model</th><th>Landscape predictor</th><th>Estimate</th><th>LC95</th><th>UC95</th></tr></thead><tbody><tr><td rowspan="10">Herbivore abundance</td><td>100 m</td><td></td><td></td><td></td></tr><tr><td rowspan="3">model 1 <- CD + MFS + CD:MFS</td><td>CD</td><td>0.182</td><td>-0.261</td><td>0.626</td></tr><tr><td>MFS</td><td>0.031</td><td>-0.411</td><td>0.472</td></tr><tr><td>CD:MFS</td><td>-0.406</td><td>-1.337</td><td>0.525</td></tr><tr><td rowspan="3">model 2 <- SNH + MFS + SNH:MFS</td><td>SNH</td><td>0.134</td><td>-0.277</td><td>0.545</td></tr><tr><td>MFS</td><td>0.014</td><td>-0.402</td><td>0.430</td></tr><tr><td>MFS:SNH</td><td>0</td><td>0</td><td>0</td></tr><tr><td></td><td>CD:SNH</td><td></td><td></td><td></td></tr><tr><td></td><td>CD:MFS:SNH</td><td></td><td></td><td></td></tr><tr><td rowspan="8">250 m</td><td rowspan="7">model 1 <- CD*MFS*SNH</td><td>CD</td><td>0.164</td><td>-0.330</td><td>0.657</td></tr><tr><td>MFS</td><td>-0.050</td><td>-0.592</td><td>0.493</td></tr><tr><td>CD:MFS</td><td>-0.731</td><td>-1.808</td><td>0.346</td></tr><tr><td>SNH</td><td>0.247</td><td>-0.170</td><td>0.665</td></tr><tr><td>CD:SNH</td><td>-0.057</td><td>-0.584</td><td>0.471</td></tr><tr><td>MFS:SNH</td><td>0.255</td><td>-0.438</td><td>0.949</td></tr><tr><td>CD:MFS:SNH</td><td>0</td><td>0</td><td>0</td></tr><tr><td rowspan="8">500 m</td><td rowspan="5">model 1 <- CD + SNH + CD:SNH</td><td>CD</td><td>0.040</td><td>-0.379</td><td>0.458</td></tr><tr><td>SNH</td><td>0.310</td><td>-0.078</td><td>0.698</td></tr><tr><td>CD:SNH</td><td>0.163</td><td>-0.753</td><td>1.080</td></tr><tr><td>MFS</td><td>0.043</td><td>-0.398</td><td>0.484</td></tr><tr><td>SNH</td><td>0.326</td><td>-0.087</td><td>0.739</td></tr><tr><td rowspan="2">model 2 <- MFS + SNH + MFS:SNH</td><td>MFS:SNH</td><td>-0.387</td><td>-1.257</td><td>0.482</td></tr><tr><td>CD:MFS</td><td></td><td></td><td></td></tr><tr><td></td><td>CD:MFS:SNH</td><td></td><td></td><td></td></tr><tr><td rowspan="2">1000 m</td><td rowspan="2">model 1 <- CD + MFS + CD:MFS</td><td>CD</td><td>-0.009</td><td>-0.387</td><td>0.368</td></tr><tr><td>MFS</td><td>-0.411</td><td>-0.769</td><td>-0.052</td></tr></tbody></table>
<table><thead><tr><th>Model</th><th>Landscape predictor</th><th>Estimate</th><th>LC95</th><th>UC95</th></tr></thead><tbody><tr><td></td><td>CD:MFS</td><td>-0.052</td><td>-0.360</td><td>0.256</td></tr><tr><td>model 2 <- SNH</td><td>SNH</td><td>0.324</td><td>-0.054</td><td>0.702</td></tr><tr><td></td><td>CD:SNH</td><td></td><td></td><td></td></tr><tr><td></td><td>MFS:SNH</td><td></td><td></td><td></td></tr><tr><td></td><td>CD:MFS:SNH</td><td></td><td></td><td></td></tr><tr><td colspan="5"><strong>Herbivore Shannon</strong></td></tr><tr><td>100m</td><td>CD</td><td>0.171</td><td>-0.220</td><td>0.563</td></tr><tr><td>model 1 <- CD + MFS + CD:MFS</td><td>MFS</td><td>-0.025</td><td>-0.412</td><td>0.362</td></tr><tr><td></td><td>CD:MFS</td><td>-0.424</td><td>-1.218</td><td>0.370</td></tr><tr><td>model 2 <- SNH + MFS + SNH:MFS</td><td>SNH</td><td>0.090</td><td>-0.272</td><td>0.452</td></tr><tr><td></td><td>MFS</td><td>-0.029</td><td>-0.396</td><td>0.339</td></tr><tr><td></td><td>MFS:SNH</td><td>-0.286</td><td>-0.919</td><td>0.347</td></tr><tr><td></td><td>CD:SNH</td><td></td><td></td><td></td></tr><tr><td></td><td>CD:MFS:SNH</td><td></td><td></td><td></td></tr><tr><td>250m</td><td>CD</td><td>0.180</td><td>-0.258</td><td>0.618</td></tr><tr><td></td><td>MFS</td><td>-0.136</td><td>-0.628</td><td>0.356</td></tr><tr><td></td><td>CD:MFS</td><td>-0.635</td><td>-1.546</td><td>0.276</td></tr><tr><td>model <- CD*MFS*SNH</td><td>SNH</td><td>0.196</td><td>-0.190</td><td>0.581</td></tr><tr><td></td><td>CD:SNH</td><td>-0.163</td><td>-0.604</td><td>0.277</td></tr><tr><td></td><td>MFS:SNH</td><td>0.368</td><td>-0.208</td><td>0.945</td></tr><tr><td></td><td>CD:MFS:SNH</td><td>0</td><td>0</td><td>0</td></tr><tr><td>500m</td><td>CD</td><td>0.124</td><td>-0.237</td><td>0.485</td></tr><tr><td>model 1 <- CD + SNH + CD:SNH</td><td>SNH</td><td>0.242</td><td>-0.100</td><td>0.584</td></tr><tr><td></td><td>CD:SNH</td><td>0.178</td><td>-0.614</td><td>0.970</td></tr><tr><td>model 2 <- MFS + SNH + MFS:SNH</td><td>MFS</td><td>-0.105</td><td>-0.475</td><td>0.264</td></tr><tr><td></td><td>SNH</td><td>0.236</td><td>-0.118</td><td>0.589</td></tr><tr><td></td><td>MFS:SNH</td><td>-0.135</td><td>-0.921</td><td>0.651</td></tr><tr><td></td><td>CD:MFS</td><td></td><td></td><td></td></tr><tr><td></td><td>CD:MFS:SNH</td><td></td><td></td><td></td></tr><tr><td>1000m</td><td>CD</td><td>0.134</td><td>-0.148</td><td>0.417</td></tr><tr><td>model 1 <- CD + MFS + CD:MFS</td><td>MFS</td><td><strong>-0.435</strong></td><td><strong>-0.716</strong></td><td><strong>-0.154</strong></td></tr><tr><td></td><td>CD:MFS</td><td>-0.005</td><td>-0.241</td><td>0.231</td></tr><tr><td>model 2 <- SNH</td><td>SNH</td><td>0.143</td><td>-0.208</td><td>0.494</td></tr><tr><td></td><td>CD:SNH</td><td></td><td></td><td></td></tr><tr><td></td><td>MFS:SNH</td><td></td><td></td><td></td></tr><tr><td></td><td>CD:MFS:SNH</td><td></td><td></td><td></td></tr></tbody></table>

Predator
<table><thead><tr><th>Model</th><th>Landscape predictor</th><th>Estimate</th><th>LC95</th><th>UC95</th></tr></thead><tbody><tr><td rowspan="10">abundance</td><td>100m</td><td>CD</td><td>-0.064</td><td>-0.452</td><td>0.323</td></tr><tr><td>model 1 <- CD + MFS + CD:MFS</td><td>MFS</td><td>-0.058</td><td>-0.424</td><td>0.309</td></tr><tr><td>model 1 <- CD + MFS + CD:MFS</td><td>CD:MFS</td><td>-0.397</td><td>-1.181</td><td>0.387</td></tr><tr><td>model 2 <- SNH + MFS + SNH:MFS</td><td>SNH</td><td>0.002</td><td>-0.348</td><td>0.352</td></tr><tr><td>model 2 <- SNH + MFS + SNH:MFS</td><td>MFS</td><td>-0.007</td><td>-0.371</td><td>0.357</td></tr><tr><td>model 2 <- SNH + MFS + SNH:MFS</td><td>MFS:SNH</td><td>-0.503</td><td>-1.062</td><td>0.057</td></tr><tr><td>250m</td><td>CD</td><td>-0.316</td><td>-0.770</td><td>0.138</td></tr><tr><td>model 1 <- CD + MFS*SNH</td><td>MFS</td><td>0.050</td><td>-0.501</td><td>0.601</td></tr><tr><td>model 1 <- CD + MFS*SNH</td><td>CD:MFS</td><td>-0.746</td><td>-1.568</td><td>0.076</td></tr><tr><td>model 2 <- CD + MFS*SNH</td><td>SNH</td><td>0.006</td><td>-0.395</td><td>0.407</td></tr><tr><td>model 2 <- CD + MFS*SNH</td><td>CD:SNH</td><td>-0.035</td><td>-0.467</td><td>0.397</td></tr><tr><td>model 2 <- CD + MFS*SNH</td><td>MFS:SNH</td><td>-0.561</td><td>-1.256</td><td>0.134</td></tr><tr><td>model 2 <- CD + MFS*SNH</td><td>CD:MFS:SNH</td><td>-1.491</td><td>-3.597</td><td>0.616</td></tr><tr><td>500m</td><td>CD</td><td>-0.141</td><td>-0.483</td><td>0.202</td></tr><tr><td>model 1 <- CD + SNH + CD:SNH</td><td>SNH</td><td>-0.052</td><td>-0.397</td><td>0.293</td></tr><tr><td>model 1 <- CD + SNH + CD:SNH</td><td>CD:SNH</td><td>-0.100</td><td>-0.902</td><td>0.701</td></tr><tr><td>model 2 <- MFS + SNH + MFS:SNH</td><td>MFS</td><td>0.248</td><td>-0.086</td><td>0.582</td></tr><tr><td>model 2 <- MFS + SNH + MFS:SNH</td><td>SNH</td><td>0.020</td><td>-0.375</td><td>0.414</td></tr><tr><td>model 2 <- MFS + SNH + MFS:SNH</td><td>MFS:SNH</td><td>-0.374</td><td>-1.094</td><td>0.345</td></tr><tr><td>1000m</td><td>CD</td><td>-0.237</td><td>-0.545</td><td>0.071</td></tr><tr><td>model 1 <- CD + MFS + CD:MFS</td><td>MFS</td><td>0.255</td><td>-0.053</td><td>0.563</td></tr><tr><td>model 1 <- CD + MFS + CD:MFS</td><td>CD:MFS</td><td>0.002</td><td>-0.250</td><td>0.255</td></tr><tr><td>model 2 <- SNH</td><td>SNH</td><td>0</td><td>-0.341</td><td>0.341</td></tr><tr><td>Predator<br>Shannon</td><td>100m</td><td>CD</td><td>-0.078</td><td>-0.477</td><td>0.321</td></tr><tr><td>model 1 <- CD + MFS + CD:MFS</td><td>MFS</td><td>-0.031</td><td>-0.410</td><td>0.348</td></tr><tr><td>model 1 <- CD + MFS + CD:MFS</td><td>CD:MFS</td><td>-0.431</td><td>-1.232</td><td>0.371</td></tr><tr><td>model 2 <- SNH + MFS + SNH</td><td>SNH</td><td>-0.189</td><td>-0.539</td><td>0.160</td></tr></tbody></table>
<table><thead><tr><th>Model</th><th>Landscape predictor</th><th>Estimate</th><th>LC95</th><th>UC95</th></tr></thead><tbody><tr><td rowspan="2">SNH:MFS</td><td>MFS</td><td>0.069</td><td>-0.309</td><td>0.447</td></tr><tr><td>MFS:SNH</td><td>-0.579</td><td>-1.117</td><td>-0.041</td></tr><tr><td colspan="5" style="background-color: #e0e0e0;">CD:SNH</td></tr><tr><td colspan="5" style="background-color: #e0e0e0;">CD:MFS:SNH</td></tr><tr><td rowspan="6">250m</td><td>CD</td><td>-0.189</td><td>-0.616</td><td>0.238</td></tr><tr><td>MFS</td><td>0.095</td><td>-0.386</td><td>0.575</td></tr><tr><td>CD:MFS</td><td>-0.617</td><td>-1.508</td><td>0.275</td></tr><tr><td>SNH</td><td>-0.187</td><td>-0.557</td><td>0.183</td></tr><tr><td>CD:SNH</td><td>0.018</td><td>-0.444</td><td>0.480</td></tr><tr><td>MFS:SNH</td><td>-0.522</td><td>-1.473</td><td>0.430</td></tr><tr><td rowspan="6">model <- CD*MFS*SNH</td><td>CD:MFS:SNH</td><td>-2.109</td><td>-4.320</td><td>0.103</td></tr><tr><td rowspan="6">500m</td><td>CD</td><td>0.040</td><td>-0.318</td><td>0.398</td></tr><tr><td>SNH</td><td>-0.195</td><td>-0.535</td><td>0.145</td></tr><tr><td>CD:SNH</td><td>0.095</td><td>-0.712</td><td>0.902</td></tr><tr><td>MFS</td><td>0.202</td><td>-0.143</td><td>0.547</td></tr><tr><td>SNH</td><td>-0.171</td><td>-0.531</td><td>0.188</td></tr><tr><td>MFS:SNH</td><td>-0.158</td><td>-0.917</td><td>0.601</td></tr><tr><td colspan="5" style="background-color: #e0e0e0;">CD:MFS</td></tr><tr><td colspan="5" style="background-color: #e0e0e0;">CD:MFS:SNH</td></tr><tr><td rowspan="6">1000m</td><td>CD</td><td>-0.006</td><td>-0.340</td><td>0.329</td></tr><tr><td>MFS</td><td>0.299</td><td>-0.018</td><td>0.617</td></tr><tr><td>CD:MFS</td><td>-0.036</td><td>-0.310</td><td>0.237</td></tr><tr><td>SNH</td><td>-0.185</td><td>-0.522</td><td>0.151</td></tr><tr><td colspan="5" style="background-color: #e0e0e0;">CD:SNH</td></tr><tr><td colspan="5" style="background-color: #e0e0e0;">MFS:SNH</td></tr><tr><td colspan="5" style="background-color: #e0e0e0;">CD:MFS:SNH</td></tr><tr><td rowspan="6">Parasitoid abundance</td><td rowspan="6">100m</td><td>CD</td><td>-0.086</td><td>-0.447</td><td>0.275</td></tr><tr><td>MFS</td><td>0.016</td><td>-0.348</td><td>0.381</td></tr><tr><td>CD:MFS</td><td>-0.134</td><td>-0.487</td><td>0.218</td></tr><tr><td>SNH</td><td></td><td></td><td></td></tr><tr><td>MFS</td><td>0.025</td><td>-0.333</td><td>0.383</td></tr><tr><td>MFS:SNH</td><td>0</td><td>0</td><td>0</td></tr><tr><td colspan="5" style="background-color: #e0e0e0;">CD:SNH</td></tr><tr><td colspan="5" style="background-color: #e0e0e0;">CD:MFS:SNH</td></tr></tbody></table>
<table><thead><tr><th>Model</th><th>Landscape predictor</th><th>Estimate</th><th>LC95</th><th>UC95</th></tr></thead><tbody><tr><td rowspan="6">250m</td><td>CD</td><td>0.216</td><td>-0.182</td><td>0.614</td></tr><tr><td>MFS</td><td>0.341</td><td>-0.049</td><td>0.730</td></tr><tr><td>CD:MFS</td><td>0.020</td><td>-0.889</td><td>0.928</td></tr><tr><td>model &lt;- CD*MFS*SNH</td><td>SNH</td><td>-0.277</td><td>-0.747</td><td>0.193</td></tr><tr><td></td><td>CD:SNH</td><td>-0.305</td><td>-0.698</td><td>0.089</td></tr><tr><td></td><td>MFS:SNH</td><td>-0.261</td><td>-0.799</td><td>0.277</td></tr><tr><td></td><td>CD:MFS:SNH</td><td></td><td></td><td></td></tr><tr><td rowspan="6">500m</td><td>CD</td><td>-0.416</td><td>-0.852</td><td>0.020</td></tr><tr><td>model 1 &lt;- CD + SNH + CD:SNH</td><td>SNH</td><td>0.074</td><td>-0.269</td><td>0.417</td></tr><tr><td></td><td><strong>CD:SNH</strong></td><td><strong>0.885</strong></td><td><strong>0.231</strong></td><td><strong>1.538</strong></td></tr><tr><td></td><td>MFS</td><td>0.222</td><td>-0.126</td><td>0.570</td></tr><tr><td>model 2 &lt;- MFS + SNH + MFS:SNH</td><td>SNH</td><td>-0.024</td><td>-0.403</td><td>0.355</td></tr><tr><td></td><td>MFS:SNH</td><td>0.041</td><td>-0.754</td><td>0.836</td></tr><tr><td></td><td>CD:MFS</td><td></td><td></td><td></td></tr><tr><td></td><td>CD:MFS:SNH</td><td></td><td></td><td></td></tr><tr><td rowspan="6">1000m</td><td>CD</td><td>-0.269</td><td>-0.600</td><td>0.062</td></tr><tr><td>model 1 &lt;- CD + MFS + CD:MFS</td><td>MFS</td><td>0.042</td><td>-0.304</td><td>0.388</td></tr><tr><td></td><td>CD:MFS</td><td>-0.187</td><td>-0.454</td><td>0.080</td></tr><tr><td>model 2 &lt;- SNH</td><td>SNH</td><td>-0.015</td><td>-0.371</td><td>0.341</td></tr><tr><td></td><td>CD:SNH</td><td></td><td></td><td></td></tr><tr><td></td><td>MFS:SNH</td><td></td><td></td><td></td></tr><tr><td></td><td>CD:MFS:SNH</td><td></td><td></td><td></td></tr><tr><td rowspan="6">Parasitoid Shannon</td><td>CD</td><td>0.072</td><td>-0.303</td><td>0.448</td></tr><tr><td>model 1 &lt;- CD + MFS + CD:MFS</td><td>MFS</td><td>0.057</td><td>-0.296</td><td>0.409</td></tr><tr><td></td><td>CD:MFS</td><td>-0.429</td><td>-1.160</td><td>0.302</td></tr><tr><td>model 2 &lt;- SNH + MFS + SNH:MFS</td><td>SNH</td><td>0.104</td><td>-0.226</td><td>0.433</td></tr><tr><td></td><td>MFS</td><td>0.099</td><td>-0.248</td><td>0.446</td></tr><tr><td></td><td>MFS:SNH</td><td>-0.486</td><td>-1.007</td><td>0.035</td></tr><tr><td></td><td>CD:SNH</td><td></td><td></td><td></td></tr><tr><td></td><td>CD:MFS:SNH</td><td></td><td></td><td></td></tr><tr><td rowspan="6">250m</td><td>CD</td><td>0.113</td><td>-0.260</td><td>0.487</td></tr><tr><td>MFS</td><td>0.223</td><td>-0.141</td><td>0.588</td></tr><tr><td>model &lt;- CD*MFS*SNH</td><td>CD:MFS</td><td>-0.303</td><td>-1.139</td><td>0.533</td></tr><tr><td></td><td>SNH</td><td>-0.012</td><td>-0.493</td><td>0.470</td></tr><tr><td></td><td>CD:SNH</td><td>-0.351</td><td>-0.734</td><td>0.032</td></tr></tbody></table>
<table><thead><tr><th>Model</th><th>Landscape predictor</th><th>Estimate</th><th>LC95</th><th>UC95</th></tr></thead><tbody><tr><td></td><td>MFS:SNH</td><td>0.158</td><td>-0.374</td><td>0.691</td></tr><tr><td></td><td>CD:MFS:SNH</td><td></td><td></td><td></td></tr><tr><td rowspan="6">500m</td><td>CD</td><td>-0.183</td><td>-0.507</td><td>0.140</td></tr><tr><td>SNH</td><td>0.126</td><td>-0.198</td><td>0.450</td></tr><tr><td>CD:SNH</td><td>0.144</td><td>-0.595</td><td>0.884</td></tr><tr><td>MFS</td><td>0.144</td><td>-0.191</td><td>0.479</td></tr><tr><td>SNH</td><td>0.141</td><td>-0.201</td><td>0.483</td></tr><tr><td>MFS:SNH</td><td>0.165</td><td>-0.557</td><td>0.886</td></tr><tr><td></td><td>CD:MFS</td><td></td><td></td><td></td></tr><tr><td></td><td>CD:MFS:SNH</td><td></td><td></td><td></td></tr><tr><td rowspan="6">1000m</td><td>CD</td><td>-0.197</td><td>-0.507</td><td>0.114</td></tr><tr><td>MFS</td><td>-0.176</td><td>-0.489</td><td>0.137</td></tr><tr><td>CD:MFS</td><td>0.036</td><td>-0.223</td><td>0.296</td></tr><tr><td>SNH</td><td>0.069</td><td>-0.257</td><td>0.394</td></tr><tr><td></td><td>CD:SNH</td><td></td><td></td><td></td></tr><tr><td></td><td>MFS:SNH</td><td></td><td></td><td></td></tr><tr><td></td><td>CD:MFS:SNH</td><td></td><td></td><td></td></tr></tbody></table>
Supplementary Figures

<figure><img src="image_11.png" /><figcaption>**Fig. S1.** (A) Research area (Hainan province) and (B) the location of the 17 sampled rice fields.</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>**Fig. S2.** Correlation graphs between the second-order interaction terms CD:MFS, CD:SNH, and MFS:SNH at the 100 m spatial scale.</figcaption></figure>

CD: crop diversity; MFS: mean field size; SNH: semi-natural habitat proportion.
<figure><img src="image_13.png" /></figure>

**Fig. S3.** Correlation graphs between the second-order interaction terms CD:MFS, CD:SNH, and MFS:SNH at the 250 m spatial scale.

CD: crop diversity; MFS: mean field size; SNH: semi-natural habitat proportion.

<figure><img src="image_14.png" /></figure>

**Fig. S4.** Correlation graphs between the second-order interaction terms CD:MFS, CD:SNH, and MFS:SNH at the 500 m spatial scale.

CD: crop diversity; MFS: mean field size; SNH: semi-natural habitat proportion.
<figure><img src="image_15.png" /><figcaption>**Fig. S5.** Correlation graphs between the second-order interaction terms CD:MFS, CD:SNH, and MFS:SNH at the 1000 m spatial scale.</figcaption></figure>

CD: crop diversity; MFS: mean field size; SNH: semi-natural habitat proportion.

<figure><img src="image_16.png" /><figcaption>**Fig. S6.** Landcover maps of the 17 study sites.</figcaption></figure>

Wenchang City, Hainan Province, China. Landscape 10 is also shown in **Fig. 1**.
<figure><img src="image_17.png" /><figcaption>**Fig. S7.** Location and landscape composition (1,000 m scale) of the 17 study sites. Wenchang City, Hainan Province, China.</figcaption></figure>
<figure><img src="image_18.png" /><figcaption>**Fig. S8.** Relative effects of landscape predictors on the diversity and total abundance of all arthropods caught in rice fields (herbivores, predators, parasitoids and neutral insects).</figcaption></figure>

Relative effect estimates of crop diversity (CD), mean field size (MFS), and semi-natural habitat proportion (SNH), as well as the second-order and third-order interaction terms (CD: MFS, MFS:SNH, CD:MFS:SNH) on the diversity (A-D) and total abundance (E-H) of all arthropods across four spatial scales: (A, E) 100 m, (B, F) 250 m, (C, G) 500 m, and (D, H) 1000 m. Predictor estimates (plain dots) and 95% CI (horizontal arrows) and names are shown in three colours: black = crop heterogeneity (CD, MFS), gold = semi-natural habitat proportion (SNH), cyan = interactive effects between crop heterogeneity and SNH (CD:MFS, CD: SNH, CD: MFS: SNH). AIC values of models 1 and 2 (Table 2) are shown in each panel along with the total contribution of fixed effects (CD, MFS, SNH and their interactions) to the variance.
<figure><img src="image_19.png" /><figcaption>**Fig. S9. Relative importance of landscape predictors on rice herbivore abundance.**</figcaption></figure>

Relative importance of crop diversity (CD), mean field size (MFS), and proportion of semi-natural habitat (SNH) predictors, as well as the second-order and third-order interaction terms (CD:MFS, MFS:SNH, CD:MFS:SNH) used in alternative models 1 (left) and models 2 (right) on **rice herbivore abundance** across the 4 spatial scales (100, 250, 500 and 1000 m).

Note: the alternative models 1 and 2 for each spatial scale are detailed in **Table S4**. CD was included in model 2 for the 1000 m spatial scale to calculate the relative importance of SNH (a minimum of two predictors is required to run the glmm.hp analysis).
<figure><img src="image_20.png" /><figcaption>**Fig. S10. Relative importance of landscape predictors on predator abundance.**</figcaption></figure>

Relative importance of crop diversity (CD), mean field size (MFS), and proportion of semi-natural habitat (SNH) predictors, as well as the second-order and third-order interaction terms (CD:MFS, MFS:SNH, CD:MFS:SNH) used in alternative models 1 (left) and models 2 (right) on **predator abundance** across the 4 spatial scales (100, 250, 500 and 1000 m). Note: the alternative models 1 and 2 for each spatial scale are detailed in **Table S4**. CD was included in model 2 for the 1000 m spatial scale to calculate the relative importance of SNH (a minimum of two predictors is required to run the glmm.hp analysis).
<figure><img src="image_21.png" /><figcaption>**Fig. S11. Relative importance of landscape predictors on parasitoid abundance.**</figcaption></figure>

Relative importance of crop diversity (CD), mean field size (MFS), and proportion of semi-natural habitat (SNH) predictors, as well as the second-order and third-order interaction terms (CD:MFS, MFS:SNH, CD:MFS:SNH) used in alternative models 1 (left) and models 2 (right) on **parasitoid abundance** across the 4 spatial scales (100, 250, 500 and 1000 m).

Note: the alternative models 1 and 2 for each spatial scale are detailed in **Table S4**. CD was included in model 2 for the 1000 m spatial scale to calculate the relative importance of SNH (a minimum of two predictors is required to run the glmm.hp analysis).
<figure><img src="image_22.png" /><figcaption>**Fig. S12. Relative importance of landscape predictors on Shannon diversity of rice herbivores.**</figcaption></figure>

Relative importance of crop diversity (CD), mean field size (MFS), and proportion of semi-natural habitat (SNH) predictors, as well as the second-order and third-order interaction terms (CD:MFS, MFS:SNH, CD:MFS:SNH) used in alternative models 1 (left) and models 2 (right) on **Shannon diversity of rice herbivores** across the 4 spatial scales (100, 250, 500 and 1000 m). Note: the alternative models 1 and 2 for each spatial scale are detailed in **Table S4**. CD was included in model 2 for the 1000 m spatial scale to calculate the relative importance of SNH (a minimum of two predictors is required to run the glmm.hp analysis).
<figure><img src="image_23.png" /><figcaption>**Fig. S13. Relative importance of landscape predictors on Shannon diversity of predators.**</figcaption></figure>

Relative importance of crop diversity (CD), mean field size (MFS), and proportion of semi-natural habitat (SNH) predictors, as well as the second-order and third-order interaction terms (CD:MFS, MFS:SNH, CD:MFS:SNH) used in alternative models 1 (left) and models 2 (right) on **Shannon diversity of predators** across the 4 spatial scales (100, 250, 500 and 1000 m). Note: the alternative models 1 and 2 for each spatial scale are detailed in **Table S4**. CD was included in model 2 for the 1000 m spatial scale to calculate the relative importance of SNH (a minimum of two predictors is required to run the glmm.hp analysis).
<figure><img src="image_24.png" /><figcaption>**Fig. S14. Relative importance of landscape predictors on Shannon diversity of parasitoids.**</figcaption></figure>

Relative importance of crop diversity (CD), mean field size (MFS), and proportion of semi-natural habitat (SNH) predictors, as well as the second-order and third-order interaction terms (CD:MFS, MFS:SNH, CD:MFS:SNH) used in alternative models 1 (left) and models 2 (right) on **Shannon diversity of parasitoids** across the 4 spatial scales (100, 250, 500 and 1000 m). Note: the alternative models 1 and 2 for each spatial scale are detailed in **Table S4**. CD was included in model 2 for the 1000 m spatial scale to calculate the relative importance of SNH (a minimum of two predictors is required to run the glmm.hp analysis).