# Ecological and management drivers of pest regulation via multitrophic pathways in tropical insular agroecosystems

Thibault Nève de Mévergnies, Théo Delauney, Marie-Stéphane Tixier, Camille

Gendron Hoareau, Joël Huat, Anaïs Chailleux

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Thibault Nève de Mévergnies, Théo Delauney, Marie-Stéphane Tixier, Camille Gendron Hoareau, Joël Huat, et al.. Ecological and management drivers of pest regulation via multitrophic pathways in tropical insular agroecosystems. Agriculture, Ecosystems & Environment, 2026, 397, pp.110030. 10.1016/j.agee.2025.110030. hal-05361313

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# Ecological and management drivers of pest regulation via multitrophic pathways in tropical insular agroecosystems

Thibault Nève de Mévergnies<sup>a,b,*</sup>, Théo Delauney<sup>c</sup>, Marie-Stéphane Tixier<sup>c,●</sup>, Camille Gendron Hoareau<sup>a,b</sup>, Joël Huat<sup>a,b</sup>, Anaïs Chailleux<sup>a,b</sup>

## ARTICLE INFO

### Keywords:

- Arthropod natural enemies

- Agroecology

- Conservation biological control

- Ecosystem services

- Trophic interactions

- Biodiversity drivers

- Structural path analysis

## ABSTRACT

Natural pest regulation services provided by arthropod natural enemies are a cornerstone of ecological intensification. While vegetation diversification is known to support natural enemy communities, its relative contribution and interactions with other drivers for pest regulation services remains unclear, especially in dynamic and diversified smallholder agroecosystems under the tropics. In this study, we investigate how crop and non-crop field vegetation diversity, farming practices, climatic and landscape parameters jointly shape arthropod community structure and their services in the aerial and ground strata in tropical market gardening systems on La Réunion Island. We surveyed 22 open-field market gardening systems during two seasons. Using a multitrophic, piecewise structural equation modeling approach, we assessed direct and indirect cascading effects of local and landscape factors on arthropod diversity and pest regulation services. Results show that in aerial communities, non-crop vegetation richness generated a positive bottom-up trophic cascade. It enhanced herbivore diversity which, in turn, increased natural enemy richness, ultimately reducing pest abundance and crop damage, particularly during the dry season. In contrast, insecticide use suppressed natural enemy richness and reduced the effectiveness of pest regulation. Ground-dwelling communities were more strongly influenced by landscape fragmentation, semi-natural habitat cover, and temperature, with less evidence for structured trophic cascades. Overall, beneficial cascading effects of vegetation on pest regulation services in above-ground communities may outweigh the impacts of other management and landscape drivers. These findings underscore the potential of promoting vegetation diversity and reducing pesticide use as key strategies for enhancing natural pest regulation in tropical smallholder agroecosystems.

## 1. Introduction

Agricultural intensification faces multiple ecological challenges linked to pesticide use (Tudi et al., 2021; Wan et al., 2025), soil erosion or greenhouse gas emissions (Goh, 2011; Gomiero et al., 2011). Moreover, intensive crop systems are vulnerable to extreme climatic conditions (Lotter et al., 2003; Isbell et al., 2015), invasive pest species (Fried et al., 2017), and pesticide resistances (Roush and Tabashnik, 2012). To meet increasing food demand through resilient ecological intensification, farming systems therefore need to transition to sustainable management built on ecosystem services (Matson et al., 1997). The objective of sustainable intensification is to maintain or enhance agricultural productivity over time while preserving ecological integrity, minimizing

negative environmental impacts, and supporting socio-economic viability for farmers (Duru et al., 2015; Gliessman, 2018; Wezel et al., 2014, 2009). Ecosystem services rely on functional ecological processes, which sustainable management aims to restore and harness in food production systems (Altieri et al., 1996; Gliessman, 2018). Provision and sustenance of ecosystem services is driven by biodiversity, hence agroecological practices focus on its conservation or restoration in agroecosystems (Garbach et al., 2014; Hooper et al., 2005; Ives and Carpenter, 2007; Loreau and de Mazancourt, 2013; Pennekamp et al., 2018). For instance, the stability of the pest regulation service relies on the diversity of its natural enemy providers through top-down mechanisms (Terborgh, 2015). These depend on functional complementarity, where species contribute different but complementary roles (Dainese

<footnote><sup>a</sup> CIRAD, UPR HortSys, Saint-Pierre, Réunion F-97455, France</footnote>

<footnote><sup>b</sup> HortSys, Univ Montpellier, CIRAD, Montpellier, France</footnote>

<footnote><sup>c</sup> CBGP, Institut Agro, CIRAD, INRAE, IRD, Univ Montpellier, Montpellier, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: tneve@hotmail.fr (T. Nève de Mévergnies).</footnote>
et al., 2017; Letourneau et al., 2009) and functional redundancy, whereby multiple species can perform similar functions and thus buffer against species loss (Casula et al., 2006; Peterson et al., 1998; Yachi and Loreau, 1999).

Crop diversification has proven to benefit ecological intensification through a higher resilience to perturbations along with positive bottom-up cascading effects on the trophic chain (Beillouin et al., 2021; Dainese et al., 2019; Han et al., 2022; Poveda et al., 2008; Wan et al., 2024, 2020a). The diversity of non-crop vegetal resources can also support the coexistence of higher trophic species and bolster bottom-up positive effects for ecological intensification (Hatt and Döring, 2023; Wan et al., 2020b). In forests and grasslands, Schuldt et al. (2019) demonstrated that the bottom-up effect of vegetal species richness on consumer species richness is consistently positive and mediated by increased structural and functional diversity of the vegetal communities. Herbivores typically show a stronger response to changes in vegetal diversity than carnivores or omnivores due to their closer trophic link (Scherber et al., 2010). The "enemies hypothesis" (Root, 1973; Russell, 1989) suggests that diverse vegetation enhances natural enemies by providing resources like shelter, nectar, and pollen (Gurr et al., 2017; Norris and Kogan, 2005; Wäckers and Fadamiro, 2005). Results from several meta-analyses (Letourneau et al., 2011; Wan et al., 2020b), reviews (He et al., 2019; Isbell et al., 2017; Ratnadass et al., 2012), and studies in different agroecosystems (Gurr et al., 2016; Mkenda et al., 2019; Ratnadass et al., 2021), concur that vegetal diversity in both natural and agroecosystems results in positive impacts on natural enemy communities whilst negatively affecting herbivores.

Increased trophic network complexity from vegetal diversification may also lead to unexpected outcomes, potentially resulting in ecosystem disservice. Neutral and negative effects on crop production have been documented, partly due the variability of the effects of vegetal diversification on arthropod diversity, potentially with a higher positive impact on herbivores than on natural enemies (Balzan et al., 2016; Dassou and Tixier, 2016; Ebeling et al., 2018; Eigenbrode et al., 2018; Lichtenberg et al., 2017). Next to taxonomic response variability, the composition of the crop or non-crop vegetal diversity or their spatial and temporal arrangements may have a higher final impact on pest regulation services than their diversity *per se* (Letourneau, 1987; Tougeron et al., 2022; Vogel et al., 2023). Also, non-crop vegetal diversification is often implemented and evaluated through agroecological infrastructures such as hedges or sown wildflower strips composed of selected species (Albrecht et al., 2020; Burel, 1996; Marshall and Moonen, 2002; Van Vooren et al., 2018). However, spontaneously occurring vegetal species (i.e. weeds) could ultimately prove beneficial for production, granted they can provide benefits to trophic cascades leading to pest regulation that trade-off potential disservices linked to competition with crops (Adeux et al., 2019; Bahadur et al., 2015; Barbercheck and Wallace, 2021; Blaix et al., 2018; Korav et al., 2018).

In addition to vegetal diversification, the diversity of arthropods in crops and the provision of pest regulation service may also be affected directly or indirectly by other local management factors such as insecticide treatments, soil tillage, mulching or field size. Large crop fields can hinder predator dispersal to vital natural habitats (Boetzl et al., 2024), whilst benefiting pests thriving on abundant crop resources and released from predation pressure (Gagic et al., 2021). Arthropod responses to disturbances generally depend on their ecological niches, underscoring the importance of assessing the effects of management practices at different trophic levels across different habitats (Elmqvist et al., 2003; Hashimoto et al., 2019; Thorbek and Bilde, 2004; Vázquez and Simberloff, 2002). Besides local factors, context-dependent factors including landscape composition (e.g. Shannon habitat diversity index, semi-natural surface cover) and configuration (e.g. habitat patch fragmentation) also shape arthropod diversity in crop systems (Bianchi et al., 2006; Chaplin-Kramer et al., 2011; Chaplin-Kramer and Kremen, 2012; Dominik et al., 2018; Martin et al., 2019). Underlying mechanisms include sink-source metapopulation dynamics which depend on abiotic

barriers, niche availability, competition, species-specific dispersal capacities and disturbance tolerance (Hanski, 1998; MacArthur and Wilson, 2001). Landscape effects often interact with local factors (Galloway et al., 2021; Tscharntke et al., 2012) and can vary with spatial scale (distance from the field of interest) (Martin et al., 2016; Zhang et al., 2020).

Despite growing evidence for the benefits of vegetation diversification in agricultural systems, its relative contribution and interaction with other local management, climatic and landscape factors driving natural pest regulation services remain unclear. This question is especially pressing in tropical agroecosystems, which typically face distinct ecological and agronomic challenges compared to temperate systems (Andres and Bhullar, 2016; Benkeblia and Francis, 2015; Côte et al., 2022; Ewel, 1986; Scopel et al., 2013). For instance, they are confronted to a year-round high pest and pathogen pressure due to the absence of winter die-off and mostly favorable abiotic conditions (Cilas et al., 2016; Ewel, 1986; Holliday, 1995). These problematics are compounded by strong rainfall seasonality, acidic, nutrient-leached soils, and a highly heterogeneous mosaic of smallholder crop systems (Dabin, 1985; Fageria and Baligar, 2003; Leakey, 2020; Morton, 2007).

To address these knowledge gaps, this study investigates how local and landscape factors jointly influence arthropod diversity and pest regulation services across trophic levels in smallholder market gardening systems on La Réunion Island, a tropical French overseas territory. These agroecosystems provide an ideal case study to evaluate the mechanisms and drivers underpinning natural pest regulation under real-world tropical conditions. We adopt a systemic and multitrophic approach to understand how local vegetation diversity supports natural pest regulation, and how this is moderated by management and landscape context. Using piecewise structural equation modeling (PSEM), we analyze direct and indirect cascading effects across trophic levels in both aerial and ground-dwelling communities. Our aim is to identify the relative contributions of ecological and management drivers to pest regulation services and to provide mechanistic insights that may guide sustainable pest management strategies in tropical agroecosystems. We hypothesize that (i) local plant diversity promotes bottom-up trophic cascades supporting natural enemies, (ii) greater natural enemy diversity enhances pest regulation, and (iii) these effects may be moderated, but not outweighed, by landscape, climatic, or other local management factors. This framework allows us to disentangle the pathways sustaining pest regulation in complex, year-round productive systems typical of tropical smallholder agriculture.

## 2. Material and methods

### 2.1. Study sites

La Réunion is a tropical volcanic island in the Indian Ocean presenting a wide range of climatic conditions owing to the altitudinal gradient, terrain topology and oceanic influence. We selected 22 market gardening fields in the southern part of the island (Fig. 1, Table 1). They were located at an altitude ranging from 71 m and 1362 m and received a mean annual rainfall ranging from 1000 mm to over 3000 mm. These fields belonged to 21 different farmers and were selected to encompass a wide range of practices, a diversity in the surrounding landscape and different climatic conditions. We selected fields growing at least one of the four main market gardening crop families (Solanaceae, Cucurbitaceae, Asteraceae and Brassicaceae).

### 2.2. Field surveys

Fields were monitored at the end of the cold and dry season (August-October 2022) and in the hot and humid season (March-April 2023) to a total sample of N = 44. At each site and for all the dates, we monitored (i) the field characteristics and management practices, (ii) the local vegetal diversity, (iii) the arthropod diversity, (iv) the pest damage to
<figure><img src="image_2.png" /><figcaption>Fig. 1. Map of the island of La Réunion presenting the main agricultural production zones (green zones) and the location of the 22 sampled fields (red dots).</figcaption></figure>

<table><caption>**Table 1**

General characteristics of the sampled fields.</caption><thead><tr><th>Farm</th><th>Latitude</th><th>Longitude</th><th>Altitude (m)</th><th>General management practices</th><th>Plot surface area (m²)</th><th>Main crop cold season</th><th>Total crops on field cold season</th><th>Main crop hot season</th><th>Total crops on field hot season</th></tr></thead><tbody><tr><td>CH_10</td><td>-21.2361720</td><td>55.5128210</td><td>967</td><td>conventional</td><td>1821</td><td>Cabbage</td><td>1</td><td>Tomato</td><td>1</td></tr><tr><td>CH_3</td><td>-21.2498580</td><td>55.5012490</td><td>664</td><td>conventional</td><td>713</td><td>Eggplant</td><td>1</td><td>Cabbage</td><td>1</td></tr><tr><td>CH_5</td><td>-21.2105873</td><td>55.5428760</td><td>1307</td><td>conventional</td><td>1864</td><td>Cucumber</td><td>1</td><td>Fallow</td><td>0</td></tr><tr><td>CH_6</td><td>-21.3092280</td><td>55.5806200</td><td>1022</td><td>conventional</td><td>238</td><td>Zucchini</td><td>1</td><td>Pepper</td><td>2</td></tr><tr><td>CH_8</td><td>-21.2624780</td><td>55.5804860</td><td>1362</td><td>conventional</td><td>2048</td><td>Pumpkin</td><td>2</td><td>Zucchini</td><td>2</td></tr><tr><td>CH_9</td><td>-21.2723530</td><td>55.5586350</td><td>1037</td><td>conventional</td><td>1057</td><td>Tomato</td><td>1</td><td>Cabbage</td><td>1</td></tr><tr><td>CL_1</td><td>-21.3422988</td><td>55.5873863</td><td>566</td><td>conventional</td><td>699</td><td>Cabbage</td><td>1</td><td>Potato</td><td>1</td></tr><tr><td>CL_10</td><td>-21.3091600</td><td>55.4459370</td><td>71</td><td>conventional</td><td>2336</td><td>Tomato</td><td>1</td><td>Fallow</td><td>0</td></tr><tr><td>CL_2</td><td>-21.3416258</td><td>55.5779930</td><td>541</td><td>conventional</td><td>370</td><td>Pumpkin</td><td>1</td><td>Potato</td><td>1</td></tr><tr><td>CL_3</td><td>-21.0784710</td><td>55.2594090</td><td>373</td><td>conventional</td><td>3578</td><td>Cucumber</td><td>1</td><td>Butternut</td><td>1</td></tr><tr><td>OH_1</td><td>-21.0967760</td><td>55.2908480</td><td>677</td><td>organic</td><td>360</td><td>Pumpkin</td><td>2</td><td>Salad</td><td>3</td></tr><tr><td>OH_12</td><td>-21.2402230</td><td>55.5340780</td><td>993</td><td>organic</td><td>421</td><td>Cabbage</td><td>2</td><td>Salad</td><td>3</td></tr><tr><td>OH_2</td><td>-21.3291020</td><td>55.5800960</td><td>738</td><td>organic</td><td>139</td><td>Salad</td><td>8</td><td>Eggplant</td><td>4</td></tr><tr><td>OH_3</td><td>-21.2205438</td><td>55.5293534</td><td>1143</td><td>organic</td><td>295</td><td>Cabbage</td><td>3</td><td>Pepper</td><td>4</td></tr><tr><td>OH_5</td><td>-21.3133530</td><td>55.5783590</td><td>945</td><td>organic</td><td>201</td><td>Salad</td><td>8</td><td>Pumpkin</td><td>2</td></tr><tr><td>OH_6</td><td>-21.0426950</td><td>55.2782900</td><td>422</td><td>organic</td><td>220</td><td>Pepper</td><td>1</td><td>Pepper</td><td>5</td></tr><tr><td>OL_1</td><td>-21.2503850</td><td>55.4935168</td><td>571</td><td>organic</td><td>730</td><td>Eggplant</td><td>3</td><td>Eggplant</td><td>1</td></tr><tr><td>OL_2</td><td>-21.0332270</td><td>55.2343970</td><td>79</td><td>organic</td><td>360</td><td>Pepper</td><td>3</td><td>Pepper</td><td>3</td></tr><tr><td>OL_3</td><td>-21.3193000</td><td>55.4852000</td><td>153</td><td>organic</td><td>244</td><td>Tomato</td><td>1</td><td>Melon</td><td>3</td></tr><tr><td>OL_4</td><td>-21.3403250</td><td>55.5211370</td><td>249</td><td>organic</td><td>362</td><td>Salad</td><td>3</td><td>Salad</td><td>2</td></tr><tr><td>OL_5</td><td>-21.3443029</td><td>55.5294670</td><td>259</td><td>organic</td><td>96</td><td>Salad</td><td>6</td><td>Eggplant</td><td>5</td></tr><tr><td>OL_6</td><td>-21.3636570</td><td>55.5635470</td><td>223</td><td>organic</td><td>108</td><td>Eggplant</td><td>1</td><td>Fallow</td><td>0</td></tr></tbody></table>

the crops, (v) the surrounding landscape diversity as well as (vi) the climate parameters. In the hot and humid season, three fields did not have any crops growing, thus were excluded from pest observation and crop damage analysis (Table 1).

### 2.2.1. Management practices

We recorded seven categorical or quantitative management parameters. These included the general farm practices as described by the producers themselves (organic or conventional), the plot surface area from GIS data, the number of crop species grown together obtained from direct field observation, the type of fertilization used (organic, chemical

or mixed) as well as the type of mulch (organic or artificial (plastic)), the weed management practices (no management, manual or mechanical, or chemical) and the number of insecticide treatments since the beginning of the plantation.

### 2.2.2. Non-crop vegetal diversity

We categorized the plot borders as hedges, roads, fallows, wasteland, bare soil, cultivated areas or woods, and estimated their relative linear proportions in the borders directly around the plots. We assessed whether the border was perennial, and we estimated the number of main vegetal species in case of a cultivated border. When the border was a
hedge, we determined its species composition, their relative proportion and if they were blooming or not. Additionally, we evaluated non-crop herbaceous diversity in three quadrats (25 × 25 cm²) within each field and in three quadrats on three randomly selected field borders, one meter away from the crop edge. The plant species present in each of these quadrats were identified thanks to expert collaborators, online resources such as WIKTROP (https://portal.wiktrop.org/fr), the Plant-Net application (https://plantnet.org/en/), as well as information available from previous studies in similar areas (Ollivier, 2015). The vegetal species richness for both the hedge and the fields were obtained. We summed these to obtain a single value per field of total non-crop vegetation richness, as well as total non-crop flowering vegetation species richness.

### 2.2.3. Arthropod diversity and crop damage

We sampled the aerial arthropod diversity using unidirectional interception cornet traps with a surface area of 1.5 m² (Figure S2). They were originally designed by J-P Sarthou to represent a more localized sub-sample of a Malaise trap due to their smaller size, making them also resistant to strong wind conditions (Sarthou, 2009). They allow the passive capture of flying and occasionally climbing arthropods through a funneling net connected to a container filled with ethanol at 95 %, in which trapped organisms fall. Two aerial traps were set up per plot, one facing the border to catch individuals assumed to enter the plot, and one facing the crop area to catch individuals assuming to leave the plot. The two traps were placed head-to-tail next to each other and oriented in a similar east-west pattern in each field, with one facing and one against the main South-East trade winds. They were left for two weeks in total. To limit evaporation and tissue degradation, samples were first collected after one week and the containers refilled with ethanol and then left for another week. Collected aerial samples of both traps per field were grouped together for subsequent analysis. For the ground-dwelling community, three pitfall traps of 500 ml were placed in each field, evenly spaced in a diagonal line crossing the field center. They were filled with a 50/50 mixture of ethylene glycol and water for a total volume of 250 ml, in addition to a drop of neutral soap and 2.5 g of sea salt to reduce water surface tension and increase tissue conservation time. Pitfall traps were collected after seven days, simultaneously to the first aerial collection, and were also grouped together for each field.

Each arthropod individual captured was then identified to at least family level and, when possible, up to its specific identity. Identification was assisted by determination keys, including 'Les insects du monde' (Aberlenc, 2021), 'Hymenoptera of the world' (Goulet and Huber, 1993) or 'Manual of Afrotropical Diptera' (Bertone, 2019). If specific identification was not possible, we attributed each unknown taxa to a morphotype based on visual examination using a stereoscopic microscope (Nikon SMZ800N - magnification 10 times to 80 times). Each morphotype was photographed and pictures were stored in a database for further identification reference. This allowed us to obtain a morphotype richness value for each trap in each field. The abundance of each of these morphotypes was also recorded for each trap and location. Lastly, for each sampled morphotype, we determined its trophic guild (herbivore, predator or parasitoid), as well as its role as a natural enemy (negatively interacting with herbivores) or not, this based on information found in the literature. Concerning morphotypes, we extrapolated this information based on known roles at higher taxonomic levels (e.g., family), but also from personal observations from the field. When uncertainty was too high, we did not attribute a trophic guild or role to the morphotype.

Besides passive captures, arthropod pest presence on the crops was monitored on six randomly chosen plants in each field, separated by at least 5 m and not directly on the field border. On each plant, we examined both sides of three leaves of different ages (young, median, old, N = 18 per field) for pest presence and abundance. These were mainly sap-sucking insects (Hemiptera), caterpillars (Lepidoptera) or beetles (Coleoptera) such as chrysomelids (Chrysomelidae). Because of the variability of the sampled crop species, those with small diameter of

leaves (like Capsicum annuum), we examined 3-4 leaves instead of one for each leaf observation to standardize leaf-surface area throughout different crop species. Lastly, we estimated the damage of arthropods (chewing or stings from sucking insects) on all the leaves observed for pest presence (three leaves of six plants to a total N = 18 per field). For each leaf, this was recorded as a number between 0 and 10 representing an estimation of the proportion of the damaged leaf surface area. If the damage on a leaf was visually estimated to be between 1 % and 10 %, then the number 1 was given to that leaf and so on up to 10. The total leaf damage per field was then obtained as a discrete count variable by summing all leaf damage values per field.

### 2.2.4. Landscape parameters

We recorded the landscape composition and configuration around each field. Forty-four polygons corresponding to the studied plots from a Google Satellite layer image were manually created. Then, we defined around each studied field buffer zones of 100 m and 750 m from the field border (excluding the field surface area). These distances were chosen after a review of the literature which supported that they were optimal small and large scales to detect landscape effects on arthropod diversity and community composition (Gallé et al., 2022; Pascual et al., 2021; Zhang et al., 2022, 2020). We used a high precision (1.5 m) vectorized land cover map of La Réunion (Le Mézo et al., 2022) to determine the different land-uses at fine scale (28 classes) and generate landscape composition (Shannon semi-natural diversity index and semi-natural vegetal surface cover) and fragmentation (number of landscape patches) metrics around each of the 22 fields for each season. After compiling and rasterizing the vectors in QGIS Desktop 3.30.3, we used the package 'landscapemetrics' version 2.1.4 (Hesselbarth et al., 2018) in RStudio to obtain the landscape metrics for both 100 m and 750 m buffer radii.

### 2.2.5. Climate characterization

Climatic variables were computed with METEOR from Cirad's Information System SmartIS (https://smartis.re/METEOR). METEOR, with a database constructed from Meteo France's network of weather stations in La Réunion, interpolates daily weather data for any geographical coordinates on the island. The geographical coordinates used correspond to the barycenter of each field. For each field and for the 14 days of arthropod trapping, we computed an average value of the minimal mean daily temperature in °C as well as the sum of the rainfall in mm. Because temperature and altitude were highly correlated (Pearson's correlation r = -0.778, p-value < 0.001), we kept only temperature in the explanatory variables.

## 2.3. Statistical analyses

All analyses were conducted using R version 4.3.1 (R Core Team, 2013). To overview total arthropod richness and abundance differences between the two seasons, a Welch's two-sample *t*-test was used. Then, we built a hierarchical four model structure to investigate the trophic interactions in each of the aerial and the ground natural communities. As variance explained by the farm was near null, we treated each sampled farm as independent observations and used general linear models (GLM) with the season as a categorical factor in the models. Each of the four models explored different trophic levels. First (herbivore model), to investigate the factors influencing sampled herbivore morphotype richness, we considered a comprehensive set of predictors encompassing the management practices, the local (field) non-crop vegetation richness for all or only flowering plants species, the proportion of perennial or wild border, two landscape composition indices (Shannon diversity and vegetal surface cover) as well as the landscape fragmentation index at both 100 m and 750 m for all three landscape metrics, and the climatic conditions (average minimal temperature and sum of rainfall during the sampling event). Next (natural enemy model), for evaluation of the natural enemy morphotype richness, we used the
same predictors with the addition of the herbivore metrics. Then (pest model), we evaluated drivers of the abundance of pests observed on the crops by using the natural enemy diversity as explanatory variables in addition to the management, landscape and climatic factors. Lastly (crop damage model), the crop leaf damage quantification was modelled using the number of observed pests, the management metrics, the natural enemy as well as the herbivore diversities as explanatory variables. For each model, we evaluated collinearities and kept only one predictor when detected. These four models were implemented separately for the aerial community (from the aerial traps) and the ground community (from the pitfall traps). The pest and the crop leaf damage model used the same samples for both aerial and ground communities.

Because of the risk of model overfitting due to the number of variables compared to the sample size, we used a random forest algorithm to select and keep only predictors that had the most influence on the four modelled response variables. We had a conservative approach and always kept the five predictors with the most influence on the dependent variable for the final model construction step. Following this initial selection, we built and refined GLMs by implementing a stepwise model selection based on AIC (Akaike Information Criterion) to optimize their fit and parsimony. Ultimately, this combination of random forest and stepwise selection allowed us to keep predictors and models with the best explanatory power (Table 1). Finally, from these four models, we used piecewise structural equation modelling (PSEM) to evaluate and hierarchize the causal relationships between vegetation, herbivore morphotype richness, natural enemy morphotype richness, pest abundance and crop damage. This path analysis framework allowed us to model ecological relations with the integration of direct, indirect, and interaction effects between non-normal (such as count data) most influent variables of interest (Table 1). From the initial results of the PSEM, manual corrections were done on the four GLMs composing the path analysis. For instance, tests of directed separation (Lefcheck, 2016) detected any missing relationships between variables that were then implemented. Final PSEM were then built, and their standardized path estimates were used to discuss and compare relations between predictors and dependent variables. Random forest selection was done with the package *Boruta* (Kursa and Rudnicki, 2010). Model selection and

stepwise AIC procedures were implemented through the MASS (Venables and Ripley, 2002) and MuMln (Kamil Bartoń, 2010) packages. We used GLMs with a negative binomial error structure to accommodate for overdispersed count data (Cameron and Trivedi, 2013). Piecewise SEM was done with the package piecewiseSEM (Lefcheck, 2016). All figures were done using ggplot2 (Wickham, 2016) and ggpubr (Kassambara, 2018) packages.

## 3. Results

Along with the four response variables of interest (natural enemy morphotype richness, herbivore morphotype richness, number of pests observed on the crops, and crop leaf damage) for the aerial and the ground samples, the predictor selection process through random forest and most parsimonious model selection retained 13 most influential factors (Table 2). These were then implemented in the PSEM analysis.

### 3.1. Total arthropod diversity

In total, we categorized arthropods into 1220 different morphotypes, representing a total of 403,404 individuals. Over 94 % (383,093) of these specimens were caught in ground pitfall traps, mainly composed of Collembola, Acari and Formicidae. On average, more morphotypes were found in the fields during the hot and humid season (mean ± SE: 145 ± 10) compared to the cold and dry season (mean ± SE: 117 ± 8) (Figure S2), but this difference was significant only for aerial traps (Figure S2A. t = -2.217, df = 39.740, p-value = 0.032). Conversely, more individuals were sampled on average in the cold and dry season (mean ± SE: 12005 ± 4568) compared to the hot and humid one (mean ± SE: 6331 ± 2235), though this difference was not significant.

From the aerial traps, we caught in total 360 morphotypes of herbivores, 281 parasitoids and 130 predators, representing 9373, 2055 and 2490 specimens, respectively (Table S1). From the ground traps, we caught a total of 124 herbivore, 85 parasitoid and 108 predator morphotypes representing 8201, 990 and 20,456 specimens, respectively. Therefore, we caught a total of 411 natural enemy morphotypes with a total abundance of 4545 specimens in aerial samples, and 193 natural

<caption>**Table 2**</caption>

Summary of the variables (response and predictors) selected to evaluate aerial and ground food web interactions using piecewise structural equation models following random forest predictor selection and most parsimonious GLM stepwise model selection based on AIC. Values correspond to the minimum, median, mean, and maximum values observed for each variable assessed in the total field sample independently of the season (N = 44). The season (hot and humid or cold and dry) was also implemented as a binary numerical value (1 or 2) in the structural equation models.

<table><thead><tr><th>Explanatory variables</th><th>Description</th><th>Minimum</th><th>Median</th><th>Mean</th><th>Maximum</th></tr></thead><tbody><tr><td>Insecticide treatments</td><td>Number of insecticide treatments done since the beginning of the crop growth</td><td>0</td><td>0</td><td>0.954</td><td>8.000</td></tr><tr><td>Number of species grown</td><td>Number of crop species grown together on the field</td><td>1.000</td><td>2.000</td><td>2.366</td><td>8.000</td></tr><tr><td>Number of pests observed</td><td>Sum of all pests observed on three leaves of three crops per field</td><td>0</td><td>26.000</td><td>102.976</td><td>943.000</td></tr><tr><td>Total leaf damage index</td><td>Sum of all leaf damage observed on three leaves of three crops per field</td><td>0</td><td>24.000</td><td>35.295</td><td>226.000</td></tr><tr><td>Non-crop vegetal richness</td><td>Species richness of non-crop vegetation in the field and its adjacent border</td><td>11.000</td><td>17.000</td><td>17.570</td><td>28.000</td></tr><tr><td>Herbivore aerial morphotype richness</td><td>Morphotype richness of herbivores caught in the aerial traps</td><td>12.000</td><td>35.000</td><td>40.500</td><td>93.000</td></tr><tr><td>Herbivore ground morphotype richness</td><td>Morphotype richness of herbivores caught in the ground (Pitfall) traps</td><td>1.000</td><td>9.000</td><td>7.953</td><td>17.000</td></tr><tr><td>Natural enemy aerial morphotype richness</td><td>Morphotype richness of natural enemies caught in the aerial traps</td><td>8.000</td><td>31.500</td><td>35.045</td><td>70.000</td></tr><tr><td>Natural enemy ground morphotype richness</td><td>Morphotype richness of natural enemies caught in the ground (Pitfall) traps</td><td>1.000</td><td>14.000</td><td>14.488</td><td>33.000</td></tr><tr><td>Landscape semi-natural vegetal surface cover at 100 m</td><td>Proportion of ground occupied by semi-natural vegetation at 100 m around the field (%). This excludes cropland</td><td>0.775</td><td>24.845</td><td>29.538</td><td>68.514</td></tr><tr><td>Landscape semi-natural vegetal surface cover at 750 m</td><td>Proportion of ground occupied by semi-natural vegetation at 750 m around the field (%). This excludes cropland</td><td>9.295</td><td>31.332</td><td>39.219</td><td>80.145</td></tr><tr><td>Landscape fragmentation at 100 m</td><td>Total number of landscape patches (noncontiguous ground occupation) at 100 m around the field</td><td>8.000</td><td>36.000</td><td>40.450</td><td>88.000</td></tr><tr><td>Landscape fragmentation at 750 m</td><td>Total number of landscape patches (noncontiguous ground category occupation) at 750 m around the field</td><td>507.000</td><td>1126.000</td><td>1072.900</td><td>1854.000</td></tr><tr><td>Landscape semi-natural patch diversity at 100 m</td><td>Shannon diversity (H) of the semi-natural landscape at 100 m around the field. This excludes cropland</td><td>0</td><td>0.739</td><td>0.766</td><td>1.392</td></tr><tr><td>Landscape semi-natural patch diversity at 750 m</td><td>Shannon diversity (H) of the semi-natural landscape at 750 m around the field. This excludes cropland</td><td>0.616</td><td>1.162</td><td>1.141</td><td>1.666</td></tr><tr><td>Average minimal temperature</td><td>Average daily minimum temperature per day during the sampling event (°C)</td><td>8.360</td><td>15.990</td><td>15.800</td><td>22.340</td></tr><tr><td>Total rainfall</td><td>Total rainfall during the sampling event (mm)</td><td>2.600</td><td>17.350</td><td>32.310</td><td>106.200</td></tr></tbody></table>
enemy morphotypes with a total abundance of 21446 specimens in ground samples. The abundance of ground natural enemies is largely due to the presence of predatory ant (Hymenoptera: Formicidae) species (mainly the tropical fire ant *Solenopsis geminata* (Fabricius 1804), the big-headed ant *Pheidole megacephala* (Fabricius 1793) and *Tetramorium bicarinatum* (Nylander 1846)) caught in the ground traps. In the ground traps, herbivore diversity was mainly composed of curculionids (Coleoptera: Curculionidae) (14 morphotypes), followed by drosophilid (Diptera: Drosophilidae) (11) flies and aphids (Hemiptera: Aphididae) (10) (Table S2). Herbivore diversity in the aerial stratum was dominated by cicadellids (Hemiptera: Cicadellidae) (32 morphotypes), chrysomelids (Coleoptera: Chrysomelidae) (20) and curculionids (19). Natural enemy diversity in ground samples was dominated by ants (19 morphotypes), braconids (Hymenoptera: Braconidae) (14), and linyphiid (Araneae: Linyphiidae) spider (13). In the aerial traps, 81 morphotypes of braconid wasps were found, followed by 44 morphotypes of ichneumonid (Hymenoptera: Ichneumonidae) wasps and 34 eulophid (Hymenoptera: Eulophidae) morphotypes (Table S2).

## 3.2. Aerial community structural analysis

Herbivore aerial morphotype richness was positively driven by local non-crop vegetal richness (Fig. 2, Table S3; PSEM, standardized estimate = 0.590, p < 0.001), and temperature (std. estimate = 0.286, p = 0.036), and negatively influenced by the number of crop species grown in the field (std. estimate = -0.222, p = 0.029). An interaction effect was detected between the season and the non-crop vegetal richness (std. estimate = 1.995, p < 0.001). In the cold and dry season, non-crop vegetal richness increased aerial herbivore richness, but this was

inverted in the hot and humid season (Fig. 3A). Overall, there were significantly more sampled herbivore morphotypes in the hot and humid season (Fig. 3B; std. estimate = 2.098, p < 0.001).

Natural enemy morphotype richness was positively associated with the herbivore morphotype richness (std. estimate = 1.129, p < 0.001) and negatively associated with the number of insecticide treatments (std. estimate = -0.303, p = 0.001). There was a significant interaction between the herbivore richness and the season (std. estimate = 0.559, p < 0.001), with a stronger increase in aerial natural enemy morphotype richness per herbivore richness in the cold and dry season compared to the hot and humid season (Fig. 3C). Next, the pest abundance observed on the crops was negatively associated with natural enemy richness (std. estimate = -0.182, p = 0.002), the number of crop species grown on the field (std. estimate = -0.183, p < 0.001), the average minimal temperature (std. estimate = -0.253, p < 0.001), the number of insecticide treatments (std. estimate = -0.117, p = 0.030), as well as the semi-natural vegetation cover within 750 m (std. estimate = -0.128, p = 0.019). There was significantly less observed pest abundance in the hot and humid season (std. estimate = 0.179, p = 0.007). Finally, crop damage was positively associated with the pest abundance (std. estimate = 0.405, p < 0.001) and negatively associated with the number of crop species grown (std. estimate = -0.220, p = 0.021). The season also affected the observed crop damage, which was lower in the hot and humid season (std. estimate = 0.289, p = 0.004).

## 3.3. Ground community structural analysis

The ground-dwelling herbivore morphotype richness was found to be negatively associated with the average minimal temperature (Fig. 4;

<figure><img src="image_3.png" /></figure>

**Fig. 2.** Path diagram illustrating the direct and indirect effects of local and landscape predictors selected through random forest and model AIC on different levels of the aerial food web, including the herbivore richness, the natural enemy richness, the observed pest abundance, and the crop leaf damage. Arrows represent hypothesized causal relationships, with color and thickness corresponding to the direction and strength of the standardized path coefficients. Green arrows indicate significant positive effects; red arrows indicate significant negative effects; black arrows represent significant seasonal (absolute values) direct (full lines) and interaction (dashed lines) effects. Path coefficients are standardized and marked with significance levels (* = p < 0.05; ** = p < 0.01; *** = p < 0.001). R² values for each response variable indicate the proportion of variance explained by each final model implemented in the PSEM. Model fit statistics (Fisher's C = 23.610, df = 30, p = 0.789) indicate a good fit between the hypothesized model and the observed data.
<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Relation between the aerial herbivore morphotype richness and A. the non-crop vegetal richness in the fields and B. the season. C. Relation between aerial natural enemy and herbivore morphotype richness depending on the season. Fitted lines correspond to negative binomial generalized linear models consistent with those implemented in the structural equation models. Dotted lines, grey points, and filled boxplots represent data from the hot and humid season, while solid lines, black points, and empty boxplots represent data from the cold and dry season.</figcaption></figure>

Table S4; PSEM, standardized estimate = -0.513, p < 0.001) and the landscape fragmentation at 100 m (std. estimate = -0.461, p = 0.004), but no significant relation was identified with the non-crop vegetal richness. Like for the aerial community, more ground herbivore morphotypes were found in the hot and humid season (std. estimate = 0.388, p = 0.007). The herbivore morphotype richness was also affected by the interaction between the number of landscape patches (fragmentation) at 100 m around the fields and the season (Fig. 5; std. estimate = 0.360, p = 0.018).

To the next trophic level, the ground natural enemy morphotype richness was positively associated with this herbivore morphotype richness (std. estimate = 0.407, p < 0.001) and negatively associated with the semi-natural vegetation diversity in the landscape at 100 m (std. estimate = -0.343, p = 0.003). Pest abundance was negatively associated with the number of crop species grown (std. estimate = -0.252, p = 0.001), average minimal temperature (std. estimate = -0.340, p = 0.001), and showed significant seasonal variation (std. estimate = 0.203, p = 0.030). There was no detected link between natural enemy morphotype richness in the pitfall trips and pest abundance. Lastly, as in the aerial community, crop damage was positively associated with pest abundance (std. estimate = 0.405, p < 0.001) and negatively associated to the number of crop species grown on the field (std. estimate = -0.220, p = 0.021), in addition to a significant decrease

of crop damage in the hot and humid season (std. estimate = 0.289, p = 0.004).

## 4. Discussion

Results reveal several key drivers of arthropod communities in market gardening crops in the tropical insular ecosystems of La Réunion. Both local and landscape factors affected the trophic cascades occurring in the arthropod communities, and these differed depending on their aerial or ground habitats and on the season of sampling. The first and second hypothesizes were confirmed for the aerial community as we observed that, overall, non-crop vegetal species richness promoted trophic cascades leading to increased natural enemy diversity through increased herbivore richness, ultimately benefiting pest regulation services through a reduction of pest abundances and their damage to the crops. Our third hypothesis was partly verified, as we observed seasonal interaction affecting the trophic cascade mediating the relation between non-crop vegetation and herbivore diversity in the aerial community. Direct effects from local management practices were also detected such as the number of insecticide treatments which negatively affected pest abundance on the crops, though with a stronger negative impact on the richness of natural enemies. The latter had a stronger effect on observed pest abundance than the number of insecticide treatments. This suggests
<figure><img src="image_5.png" /><figcaption>**Fig. 4.** Path diagram illustrating the direct and indirect effects of local and landscape predictors selected through random forest and model AIC on different levels of the ground-dwelling arthropod community, including herbivore richness, natural enemy richness, pest abundance, and crop damage. Arrows represent hypothesized causal relationships, with color and thickness corresponding to the direction and strength of the standardized path coefficients. Green arrows indicate significant positive effects; red arrows indicate significant negative effects; black arrows represent significant seasonal (absolute values) direct (solid lines) and interaction (dashed lines) effects. Path coefficients are standardized and marked with significance levels (* = p < 0.05; ** = p < 0.01; *** = p < 0.001). R² values within each response variable indicate the proportion of variance explained by the final piecewise SEM. Model fit statistics (Fisher's C = 32.039, df = 30, p = 0.366) indicate a good fit between the hypothesized model and the observed data.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>**Fig. 5.** Relation between the ground herbivore morphotype richness and the number of landscape patches (fragmentation) and 100 m around the fields depending on the season. Fitted lines correspond to negative binomial generalized linear models consistent with those implemented in the structural equation models. Dotted lines and grey points represent data from the hot and humid season, while solid lines and black points represent data from the cold and dry season.</figcaption></figure>

that natural pest regulation services promoted by the non-crop vegetation cascade may, in above-ground communities, outweigh artificial pest management practices in sampled fields. Regarding the ground

community, external factors linked to the landscape, the temperature and the season dominated the structural analysis. These may have disrupted trophic cascades, although the positive relation between natural enemy and herbivore morphotype richness remained.

### 4.1. Non-crop vegetation management can promote bottom-up trophic cascades

Local non-crop vegetal species richness had a strong beneficial effect on the richness of the herbivore morphotypes caught in the aerial traps in the cold and dry season, but this was not the case in the hot and humid season. In La Réunion, temperature variation between seasons is low (~4°C), in contrast to rainfall which can be close to absent in the dry season and up to 12 m/year in certain areas in the hot season (Réchou et al., 2014). In the dry season, La Réunion's natural habitats are therefore largely constrained by rainfall for vegetation growth. In contrast, water in agricultural fields is provided through irrigation, sustaining throughout the year both non-crop and crop vegetation growth thus availability of food and habitat resources for higher trophic levels. Their scarcity in the landscape during the dry and cold season probably makes vegetation in crop fields particularly appealing to herbivores. This may generate a cross-habitat spill-over effect from the landscape to these persistent niches in crop fields, which become biodiversity sinks in that season (Blitzer et al., 2012; Pulliam, 1988; Rusch et al., 2010; Tscharntke et al., 2012). Although we implemented total rainfall during the sampling events as an initial predictor before random forest selection, this metric by itself may not have reflected the overall rainfall effect linked to the season as it was not significant. In the
ground community, we did not detect any effect of vegetation diversity on the herbivore morphotype richness sampled from pitfall traps. This could be linked to our experimental design that may have overlooked factors influencing trophic links in the ground community. For example, there may be a sampling bias linked to the pitfall trap, as these only catch species that are active (activity-density metric) during the sampling period, possibly not accounting for an important part of the arthropod community (Brown and Matthews, 2016).

Both in the aerial and ground communities and independently of the season, the herbivore morphotype richness was the most important driver of the natural enemy morphotype richness. An increase in prey diversity may allow for an increase in natural enemy diversity through functional complementarity and niche partitioning, whilst limiting intraguild competition and predation (Dainese et al., 2017; Letourneau et al., 2009; Snyder, 2019; Tixier et al., 2013). Although no direct effects of the non-crop vegetal diversity on the natural enemy communities were detected, plants indirectly bolstered pest regulation service providers, at least in the aerial community and more so in the cold season, through the intermediate herbivore trophic level. Similar bottom-up effects from plant to natural enemy diversity mediated by herbivore diversity were previously detected in mango production systems on La Réunion (Jacquot et al., 2019), as in diversified crop systems in China (Shi et al., 2014) and Pakistan (Farooq et al., 2022). Then, we observed that natural enemy morphotype richness was, in the aerial community, a negative driver of the pest abundance on the crops. This suggests a top-down predator control which can subsequently mitigate crop damage (Vidal and Murphy, 2018), as the latter was indeed found to be mainly driven by the observed pest abundance. In contrast, our results show no link between the trapped herbivore diversity and the observed pest abundances on the crops, nor on the crop foliar damage, suggesting that herbivore diversity itself does not contribute to crop disservices. Pests typically consist of a limited number of species in the overall diversity of a system, capable of outcompeting other herbivores when crop resource availability is high (Oerke, 2006; Sharma et al., 2017).

## 4.2. Relative contribution and interactions between local and landscape drivers of pest regulation services

The number of insecticide treatments had an expected negative effect on the observed pest abundance, but it was more important on the richness of natural enemies in the aerial community, and the effect of the natural enemy richness on pest abundance was higher than that of the number of insecticide treatments. Natural pest regulation by natural enemies in the aerial community may therefore outweigh artificial pest management using insecticides. Similar results had been found for aphid control in barley fields in Sweden (Östman et al., 2003). Also, we did not detect any impact of the insecticide treatments on the ground-dwelling natural enemies nor on the herbivore richness in any strata. This result suggests that certain species of natural enemies, particularly flying species, may be more sensitive to disturbances from the application of insecticide than target pest species or non-target herbivore organisms, as documented in previous studies (Bengtsson et al., 2005; Flores-Gutierrez et al., 2020; Krauss et al., 2011; Letourneau and Goldstein, 2001; Meehan et al., 2011). Low-intense management practices usually offer more services than disservices, and the benefits of pesticide reduction on pest regulation service providers detected in this study align with the literature consensus (Dainese et al., 2019; Deguine and Penvern, 2014; Letourneau and Bothwell, 2008; Letourneau and Goldstein, 2001; Lichtenberg et al., 2017; Muneret et al., 2019, 2018; Poveda et al., 2008; Rusch et al., 2016).

Concerning the aerial community, the semi-natural vegetation surface cover at 750 m around the fields had a negative impact on the observed pest abundance, as did the crop species richness grown on the fields. Crop species richness also negatively affected herbivore morphotype richness sampled in the aerial traps. Both these landscape and local drivers may be a result of the 'resource concentration hypothesis',

where more complex systems hosting higher resource diversity can lead to the dilution of specialist species thriving on abundant specific food resources (Andow, 1991; Kheirodin et al., 2024; Otway et al., 2005; Root, 1973). The observed negative effect of the crop diversity could also result from a trade-off with the non-cultivated vegetal richness, as the latter was a strong positive driver of the herbivore diversity in the aerial community. Our study design overlooked competition for resources such as space that may have been occurring between crops and non-crop vegetal communities, perhaps explaining their contrasting effects on the food chain (Korav et al., 2018; Zimdahl, 2007).

In the cold and dry season, the ground herbivore morphotype richness was negatively impacted by the landscape fragmentation at 100 m around the fields, consistent with findings from a meta-analysis based on 89 studies (Rossetti et al., 2017). Smaller habitat fragments typically host less resources thus sustain smaller densities as competition increases, which can lead to local extinctions reducing diversity (Fischer and Lindenmayer, 2007; Tscharntke et al., 2007). Extinction-colonization dynamics depend on patch isolation and habitat connectivity, to which species are not equal as this depends on their dispersal capacities (Thomas, 2000). Some ground-dwelling species may be more sensitive to habitat fragmentation due to lower dispersal capacities than flying arthropods (Holland et al., 2004; Perry et al., 2017; Schellhorn et al., 2014). The different impact of landscape parameters depending on trophic guild or taxonomic group has previously been highlighted in urban (Gibb and Hochuli, 2002) and agricultural (Dominik et al., 2018; Galloway et al., 2021; Macfadyen and Muller, 2013; Perez-Alvarez et al., 2018) environments. In the hot and humid season, habitat fragmentation did not negatively impact ground herbivores. This is probably because, in contrast to the dry season, resources in these fragmented patches were more abundant and easier to access across the landscape (Lara-Pérez et al., 2023). At the same 100 m scale, the semi-natural vegetal diversity in the landscape had a negative effect on the natural enemy morphotype richness found in ground samples independently of the season. A hypothesis could be a spillover effect seeing natural enemies move from the field to the nearby landscape for this ground-dwelling community when landscape resources are highly attractive (Rand et al., 2006). Next to ecological mechanisms linked to taxonomy, undetected variability from covariates that we did not include in our study, such as soil perturbations, could also explain why ground species appear more influenced than the aerial ones by source-sink effects with the landscape (Müller et al., 2022). In a previous study, landscape resources at 400 m around mango orchards of La Réunion had no detected effect on arthropod diversity, which authors discuss as being probably due to resource availability and arthropod dispersal capacities (Jacquot et al., 2019). Systems hosting perennial crops or infrastructures such as mango orchards may host communities that are more spatially stable in these less disturbed habitats thus less dependent on landscape resources (Cahenzli et al., 2019; Heinen et al., 2023).

Overall, more species and individuals were sampled in the hot and humid season compared to the cold and dry one. This can be explained by the increased resources linked to bolstered system productivity, as well as the faster development of these ectotherms in hotter conditions (Dixon et al., 2009; Lara-Pérez et al., 2023). In this study, temperature strongly correlated with altitude (r ~ -0.8) and always affected herbivore diversity in both aerial and ground strata, though with inverse relations. The impossibility to decouple temperature from altitudinal effects on the arthropod community is a design limitation, preventing clear interpretation of their fixed and interactive effects. Nevertheless, fields at colder temperatures thus at higher altitudes hosted less diverse aerial herbivore communities, which is consistent with general altitudinal diversity patterns observed on La Réunion (Dianzinga et al., 2020) or in other regions (González-Reyes et al., 2017; Hodkinson, 2005; Supriya et al., 2019). In contrast, the ground-dwelling herbivore morphotype richness was negatively affected by the temperature. This suggests that ground-dwelling species may be sensible to heat stress and
better acclimated to locally cooler conditions at higher altitudes (Hoffmann et al., 2013). We can also hypothesize that ground-dwelling species thrive in these harsher abiotic conditions because they may face less competition as aerial herbivore species richness decreases (Hodkinson, 2005; Sømme, 1989; Villani et al., 1999). Temperature and by extension altitude also negatively affected observed pest abundance in our study. Beyond potential heat sensitivity that may constrain pest fitness, especially during the warm tropical season, this pattern could reflect confounding factors not accounted for in our design. In particular, the identity of the crops on which pests were observed varied between fields and was influenced by climatic constraints related to both season and altitude. Temperature likely acts as a proxy for this broader variability, which includes crop type and, consequently, pest identity, factors that could not be fully disentangled in the present study.

## 5. Conclusion

This study highlights how both local and landscape factors shape arthropod trophic interactions and pest regulation services in tropical market gardening systems. Aerial communities benefited from non-crop vegetation diversity through a bottom-up cascade, with herbivore richness enhancing natural enemy diversity and ultimately reducing pest abundance and crop damage. Crop species richness also contributed positively by reducing pest abundance. These results suggest that managing both crop and non-crop vegetation in tandem and at different scales may optimize pest regulation outcomes (Hatt and Döring, 2023; Lichtenberg et al., 2017; Wan et al., 2020b). From our observations, natural regulation processes were more influential than insecticide applications. Unintended negative effects on beneficial arthropods were detected from insecticide use, underscoring the value of reducing chemical inputs where feasible (Gandara et al., 2024; Wan et al., 2025). At the landscape scale, larger and more connected semi-natural habitats were associated with lower pest abundance and crop damage. In contrast, high landscape fragmentation negatively influenced ground-dwelling communities. This supports the need to integrate landscape policies to the design of sustainable food production systems (Hatt and Döring, 2023; Redlich et al., 2021; Tcharntke et al., 2005).

Limits to this study include the confounding influence of altitude, temperature, and crop identity, which were difficult to disentangle in the field setting. Nevertheless, such complexity reflects real-world conditions in tropical agroecosystems and underscores the value of in situ ecological research. Future steps to develop ecosystem service efficiency in market gardening agroecosystems should aim to (i) identify key native plant species best bolstering cascading effects (Deguine et al., 2019; Isaacs et al., 2009). Competitive trade-offs between non-crop and crop plants should also be assessed. Also, (ii) stacking the benefits of sustainable farming practices integrating diversity spatially and/or temporally at both local and landscape scales should be developed (Duru et al., 2015; Hatt and Döring, 2023; Kremen et al., 2012; Wan et al., 2019). Then, (iii) develop simple indicators and tools for farmers to monitor and manage natural pest control (Birkhofer et al., 2018; Duru, 2013) and, lastly (iv), policymakers should be involved to promote non-crop vegetation in the fields and protect natural habitats in the landscape for an area wide service management (Thomine et al., 2022).

### CRediT authorship contribution statement

**Anaïs Chailleux:** Writing – review & editing, Validation, Supervision, Resources, Project administration, Methodology, Funding acquisition, Conceptualization.
**Joël Huat:** Supervision, Resources, Methodology, Funding acquisition, Conceptualization.
**Nève de Mévergnies Thibault:** Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.
**Camille Gendron Hoareau:** Writing – review & editing, Investigation.
**Marie-Stéphane Tixier:** Writing – review & editing, Validation, Supervision.
**Théo Delauney:** Writing –

review & editing, Methodology, Investigation, Data curation, Conceptualization.

### Consent to participate

All producers consented to the arthropod sampling and surveys of their fields.

### Consent for publication

Not applicable.

### Ethics approval

Not applicable.

### Funding

This work was led with the financial support from the French Biodiversity Agency (OFB) (agreement N°OFB.21.0955). It was carried out as part of the CIRAD DPP SADUR agronomical research program (activities 2023–2025) funded by a grant from the European Community (ERDF), the Conseil Régional de La Réunion and the Département de la Réunion.

### Code availability

Not applicable.

### Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgements

The authors would like to thank the two anonymous reviewers whose constructive comments substantially improved this manuscript. We also wish to thank all producers who authorized us to sample their crop fields. Thanks to Marie Huyghe for her help on the field and to Adrien Mathon for his help during arthropod sorting and identification. Thanks to Mirana Gauche (UMR PVBMT) for access to the arthropod collections of the Université de La Réunion and to Maxime Jacquot (GRAB Avignon) for sharing a photographic database of parasitic wasps caught on the island. Thanks to Rose-My Payet (UPR AIDA) for sharing her knowledge on non-crop vegetal identification. We are grateful to Caroline Brunel for her insight concerning management indicators.

## Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.agee.2025.110030.

### Data availability

Data will be made available on request.

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