# Exploring biotic and abiotic factors influencing cucurbit-fruit fly damage and diversity in cucurbit crop production systems

Louise Eypert, Thibaud Martin, Nahoulé Armand Adja, Emilie Deletre

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Louise Eypert, Thibaud Martin, Nahoulé Armand Adja, Emilie Deletre. Exploring biotic and abiotic factors influencing cucurbit-fruit fly damage and diversity in cucurbit crop production systems. International Journal of Tropical Insect Science, 2025, 45, pp.2751-2771. 10.1007/s42690-025-01622-4. hal-05492219

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# Exploring biotic and abiotic factors influencing cucurbit-fruit fly damage and diversity in cucurbit crop production systems

Louise Eypert<fnref n="1" />,<fnref n="2" /> ID · Thibaud Martin<fnref n="1" /> ID · Nahoulé Armand Adja<fnref n="2" /> ID · Emilie Delete<fnref n="1" />,<fnref n="3" /> ID

Received: 16 August 2025 / Accepted: 26 August 2025 / Published online: 20 October 2025
© The Author(s) 2025

## Abstract

Agricultural policies in Côte d'Ivoire have mostly been focused on staple food crops such as rice and cassava. However, a diverse range of vegetables is essential for the balanced nutrition and health of the population. Cucurbits, for instance, are a regular ingredient in local cuisine, yet this product is subject to high pressure from pests such as fruit flies (Diptera: Tephritidae). Farmers try to protect their crops against these pests by applying high uncontrolled doses of the few chemical pesticides available to them. This practice is ill-adapted for managing fly infestations and there are major harmful effects. From an agroecological perspective, alternative control methods have been developed such as prophylaxis and using specific attractants in insect trapping systems. It is, however, necessary to identify and quantify the targeted pest population(s) prior to implementing any control method. The present study involved cucurbit field sampling and farmers interviews and aimed to gain further insight into various factors could explain the observed fruit fly infestations and species diversity. 90% of the surveyed cucurbit plots were infested by fruit flies and a mean of 30.5% of the cucurbit fruits were damaged. We noted that this infestation rate was mainly dependent on the location and period of the year as well as the extent of crop protection and field management practices. We identified 5 cucurbit fruit fly species with *Dacus ciliatus* found to be present on 77% of infested plots (0.63 mean relative abundance). Depending on the dominant species on the target plot, the latter index generally correlated with the infestation rate, the period of the year or the number of days since the previous crop protection treatment. The overall species diversity also depended on the number of days since the previous crop protection treatment as well as the treatment frequency index throughout the crop cycle. A more in-depth analysis of the infestation level at the fruit scale revealed preferential differences in terms of fruit weight and competition according to fruit fly species involved. This study offers an update of the current cucurbit fruit fly pest status in a West African country following as follow-up to previous inventories. The findings indicated enhancing farmers' knowledge on the pests in their cropfields and the chemical pesticides they apply, through more political support, could help them efficiently adapt their cropping system management.

**Keywords** Tephritidae · *Zeugodacus cucurbitae* · *Dacus ciliatus* · Ovisposition · Zucchini · Cucumber · Damage

## Introduction

The effective occupation of an ecological niche by a living organism is a cross-result of: abiotic factors, i.e. temperature and relative humidity that vary according to the elevation, season and local weather variability; and biotic factors including hosts and roosting plant availability and suitability, interspecific competition and natural enemies (Vayssières et al. 2008; Mwatawala et al. 2009, 2010; Ekesi et al. 2016a, b). These factors drive the relative abundance of species and their spatiotemporal fluctuations. Moreover, anthropogenic activities such as urbanization and agriculture impact these factors especially hosts and roosting plant availability, through deforestation and cultural management,

<figure><img src="image_2.png" /></figure>

<footnote>Louise Eypert
louise.eypert@cirad.fr</footnote>

<footnote n="1">¹ Centre de Coopération Internationale en Recherche Agronomique Pour Le Développement (CIRAD), UPR HORTSYS, University of Montpellier, Montpellier, France</footnote>

<footnote n="2">² UMR Sciences Agronomiques Et Procédés de Transformation, Ecole Supérieure d'Agronomie, Institut National Polytechnique Félix Houphouët-Boigny, Yamoussoukro, Côte d'Ivoire</footnote>

<footnote n="3">³ Centre Suisse de Recherche Scientifique (CSRS), Adiopodoumé, Côte d'Ivoire</footnote>
thus disturbing natural equilibria and ecosystems (Beillouin
et al. 2023).

In Côte d'Ivoire (West Africa), the tropical climatic
conditions are favourable for pest and disease proliferation
year-round, which may be further aggravated by agricultural
practices such as monocultures and the absence of crop
succession (De Bon et al. 2014). Other common practices
like bush burning destroy biodiversity and hamper natu-
ral regulation, in addition to massive uncontrolled use of
chemical pesticides whose active substances are not always
adapted or efficient while harming the soil, environmental
and human health (Doumbia and Kwadjo 2009; Oula et al.
2021). Smallholder farmers who are the cornerstone of the
market vegetable production subsector must cope with many
constraints such as climate change, land and water access,
but they also lack financial and technical (material and intel-
lectual) support at the expense of cash crops for exporta-
tion (Okigbo and Ugwu 2021). Most farmers are unable to
recognize insect pests and beneficial organisms. The market
supply of crop protection products is generally limited and
uncontrolled with door-to-door and retail salespersons often
promoting pesticides that are prohibited in other countries.
Moreover, in some regions of Côte d'Ivoire, smallholder
farmers are illiterate or speak only the local dialect, which
may lead to overuse and misuse of chemical pesticides (Oula
et al. 2021). Moreover, such phytosanitary practices are not
very efficient against target pests, including fruit flies (Dip-
tera: Tephritidae) (Kone et al. 2018; N'Dépo and My 2019).

Indeed, 250 fruit fly species of the Tephritidae fam-
ily are known to cause damage to a highly diverse range
of wild and cultivated fruits and vegetables worldwilde
(Rwomushana and Tanga 2016). Gravid females need a
suitable substrate, i.e. just under the skin of fleshly fruit
and vegetables, for egg laying and optimal development of
their offspring. The eggs hatch after 2–5 days and the lar-
vae eat the fruit flesh, which becomes inedible for humans,
during 8 to 12 days after which they exit the fruit and go
underground to pupate for 6–10 days before emerging as
juvenile flies (Ouedraogo 2011). Depending on the spe-
cies, females reach sexual maturity after 7–25 days and
are able to lay dozens of eggs at once with 100–1,000 eggs
laid during their 2–6 month lifespan (Vayssières 1999).
Fruit flies have different host ranges, they are generalist
to specialist species that only attack one botanical fam-
ily (e.g. Cucurbitaceae, Solanaceae). In Côte d'Ivoire, the
most widely cultivated cucurbits are cucumbers, which are
available throughout the year, and zucchinis which more
vulnerable to seasonal and economic fluctuations, and fly
attacks. Gourd and margose fruit are seldom marketed and
are mostly gathered in the wild (personal data and com-
munication). All of these vegetables are major produce
on local markets, while being essential in people's diet.
In addition, due to global trade and import/export, other

exotic and invasive species may gain a foothold and spread
in new areas and may compete with and replace indig-
enous species, while sometimes being a source of pests
that can damage local previously pest-free plant species. In
Réunion Island, the oriental fruit fly *Bactrocera dorsalis*
has replaced *Bactrocera zonata* since its introduction in
2017 (Moquet et al. 2020).

Given all of the above considerations, it is now clearly
essential to adopt an agroecological cropping approach to
replace the current agricultural management model so as
to restore natural processes and thereby support sustainable
agricultural production. In this perspective, pest control can
be achieved by combining practices and methods at different
spatiotemporal scales to reduce the pest populations below
their harmful threshold, a strategy that is commonly referred
to as integrated pest management (IPM) (Deguine et al.
2021). However, this holistic strategy requires knowledge
and a clear understanding on the bioecology of the prevail-
ing pests (Mausse and Bandeira 2007).

The present was carried out to assess the biotic and abi-
otic factors influencing (i) crop damage (infestation rate and
level) and (ii) fruit fly diversity (species richness, species
abundance and relative species abundance), at the plot and
fruit scales. More specifically, the species abundance was
taken into account characterize respective oviposition prefer-
ences of females of different fruit fly species for some factors
at the plot and fruit scales. The relative abundance was then
considered to assess the extent of competition between these
species at the plot and fruit scales.

**Material and methods**

Field surveys and samplings

10 to 15 plots located in each of the four periurban zones
of Korogho, Bouaké, Yamoussoukro and Abidjan between
northern to southern Côte d'Ivoire were monitored, sur-
veyed and sampled regularly from February to October
2023. At least one cucurbit species (cucumber and/or zuc-
chini), at the fructification stage, was being cropped in
the sampled plot. Two complementary approaches were
used to assess the extent of damage (infestation rate and
level) and fruit fly diversity (species richness, species and
relative abundance): (i) counting of potentially infested
(i.e. with at least one visible oviposition puncture) fruit
on the plot, and (ii) collection of infested fruit for incuba-
tion in the lab until pupation and fly emergence. To assess
the influence of abiotic and biotic factors, a survey on
the growers' agricultural practices was conducted in addi-
tion to direct visual assessments of the plot surroundings
(Table 1) and lab measurements of the sampled fruit char-
acteristics (Table 2). We recorded the location (4 cities)

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<table><caption>**Table 1** Group, name and values of all studied variables at the plot scale</caption><thead><tr><th>Variable group</th><th>Variable name</th><th>Definition</th><th>Values/Levels</th></tr></thead><tbody><tr><td rowspan="3">Response variables</td><td>infestrate</td><td>Percentage of infested fruit/plot</td><td>Integer from 0 to 100%</td></tr><tr><td>nbspc</td><td>Number of different fly species/plot (species richness)</td><td>Integer from 0 to 5</td></tr><tr><td>raX</td><td>Proportion of species X fruit fly individuals that emerged from all sampled fruit of one plot relative to the total number of flies that emerged from the same plot (relative abundance)</td><td>Integer from 0 to 1</td></tr><tr><td rowspan="3">Global/climatic</td><td>nbXfruits</td><td>Average number of species X fruit fly individuals that emerged from 10 fruits collected/plot</td><td>Integer from 0 to 480</td></tr><tr><td>city</td><td>Sampling location</td><td>abj (Abidjan), bke (Bouaké), Yakro (Yamoussoukro), kgo (Korhogo)</td></tr><tr><td>quarter</td><td>Sampling date</td><td>T1 (February to April), T2 (May to July), T3 (August to October)</td></tr><tr><td rowspan="2">Landscape/environment</td><td>domlandscape</td><td>Large-scale dominant landscape</td><td>nat (nature), plant (cash crop such as cocoa or cashew), urb (urban), marketgard (market gardening), mixland (50–50 market gardening with either of the 3 other types)</td></tr><tr><td>plotenv</td><td>Plot-scale environment</td><td>Shallow, bank (lake bank), agrizone (irrigated agricultural area), insu-lated (plot bordered by walls or forest), otherenv (experimental plot or yard-type)</td></tr><tr><td rowspan="2">divers</td><td>palm</td><td>Diversification level of the farm</td><td>Mono (monoculture of the sampled cucurbit alone or in association), div (other crops on surrounding planting beds)</td></tr><tr><td>potsechost</td><td>Presence of palm trees in the vicinity of the cucurbit plot</td><td>ypalm (yes), npalm (no)</td></tr><tr><td rowspan="10">Crop management and agricultural practices</td><td>refugeplant</td><td>Presence of potential secondary vegetable hosts (fruit trees) in the vicinity of the cucurbit plot</td><td>ypsh (yes), npsh (no)</td></tr><tr><td>serviceplant</td><td>Presence of refuge plant s in the vicinity of the cucurbit plot</td><td>yrp (yes), nrp (no)</td></tr><tr><td>cucurbitedge</td><td>Presence of service plants in the vicinity of the cucurbit plot</td><td>ysp (yes), nsp (no)</td></tr><tr><td>leafyvegedge</td><td>At least one edge of the plot is cropped with cucurbit</td><td>yce (yes), nce (no)</td></tr><tr><td>vegedge</td><td>At least one edge of the plot is cropped with leafy vegetable</td><td>yve (yes), nve (no)</td></tr><tr><td>othercropedge</td><td>At least one edge of the plot is cropped with vegetable-fruit</td><td>yve (yes), nve (no)</td></tr><tr><td>potsechostedge</td><td>At least one edge of the plot is composed of potential secondary hosts (fruit tree)</td><td>yoce (yes), noce (no)</td></tr><tr><td>refugeplantedge</td><td>At least one edge of the plot is composed of refuge plants</td><td>ypshe (yes), npshe (no)</td></tr><tr><td>serviceplantedge</td><td>At least one edge of the plot is composed of service plant</td><td>yrpe (yes), nrpe (no)</td></tr><tr><td>spontedge</td><td>At least one edge of the plot is composed of weeds</td><td>yspe (yes), nse (no)</td></tr><tr><td>soiledge</td><td>At least one edge of the plot is composed of bare ground</td><td>yse (yes), nse (no)</td></tr><tr><td>artificialeedge</td><td>At least one edge of the plot is composed of human infrastructure (pathways, roads, canals)</td><td>yee (yes), nee (no)</td></tr><tr><td>wateredge</td><td>At least one edge of the plot is composed of stagnating or circulating water (river, canal)</td><td>yae (yes), nae (no)</td></tr><tr><td>cucurbit</td><td>Sampled cucurbit species</td><td>ywe (yes), nwe (no)</td></tr><tr><td>agecucurbit</td><td>Number of days after seeding</td><td>cusat (Cucumis sativus), cupep (Cucurbita pepo)</td><td>Integer from 25 to 120 days</td></tr></tbody></table>
<figure><img src="image_4.png" /><figcaption>**Table 1 (continued)**</figcaption></figure>

<table><thead><tr><th>Variable group</th><th>Variable name</th><th>Definition</th><th>Values/Levels</th></tr></thead><tbody><tr><td></td><td>asso</td><td>Association within the cucurbit plot</td><td>asso (association with another crop), pur (pure cucurbit crop)</td></tr><tr><td></td><td>color</td><td>Cucurbit skin color</td><td>Dark, Light, Both</td></tr><tr><td></td><td>skin</td><td>Cucurbit skin texture</td><td>Smooth, Hard, Both</td></tr><tr><td></td><td>surftot</td><td>Total cultivated area</td><td>Number from 0.025 to 8 ha</td></tr><tr><td></td><td>surfucubit</td><td>Cucurbit area</td><td>Number from 0.0035 to 0.8 ha</td></tr><tr><td></td><td>distplant</td><td>Distance between two cucurbit plants (planting density)</td><td>Number from 0 (random) to 225 cm</td></tr><tr><td></td><td>grasscover</td><td>Extent of weed cover around cucurbit plants</td><td>Integer from 1 (&lt;10%) to 3 (&gt;80%)</td></tr><tr><td></td><td>previouscrop</td><td>Crop cultivated just before the current cucurbit crop</td><td>Leafyveg (leafy-vegetable crop), veg (vegetable-fruit crop), spont (weeds/weeds), otherprevcrop (subsistence crop, cash crop, forest, flooding)</td></tr><tr><td></td><td>rotation</td><td>Change of botanical family from one crop cycle to the next one</td><td>yes, no</td></tr><tr><td></td><td>irrigation</td><td>Irrigation system used to water the plot</td><td>Can (watering can), hose (hose), flood (soil inundation/flooding), other-irri (drip/drop, spray, rainfall)</td></tr><tr><td></td><td>ferti</td><td>Type of fertilizers applied before sowing and/or flowering</td><td>Chemical, Organic, Both</td></tr><tr><td></td><td>treatfreq</td><td>Number of crop protection treatments/week</td><td>Integer between 1 or 2 times a week</td></tr><tr><td></td><td>treatfreqdays</td><td>Number of days between 2 treatments</td><td>Number between 2.33 and 14 days</td></tr><tr><td></td><td>lasttreat</td><td>Number of days since last treatment</td><td>Number between 0 and 60 days</td></tr><tr><td></td><td>harvestfreq</td><td>Number of harvests/week</td><td>Integer between 1 and 3 times a week</td></tr><tr><td></td><td>lastharvest</td><td>Number of days since last harvest</td><td>Integer between 0 and 20 days</td></tr><tr><td></td><td>harvestnb</td><td>Number of harvests before our visit</td><td>Integer between 0 and 8</td></tr><tr><td></td><td>py</td><td>Application of pyrethrinoids during the crop cycle</td><td>ypy (yes), npy (no)</td></tr><tr><td></td><td>ne</td><td>Application of neonicotinoids during the crop cycle</td><td>yne (yes), nne (no)</td></tr><tr><td></td><td>av</td><td>Application of avermectin during the crop cycle</td><td>yav (yes), nav (no)</td></tr><tr><td></td><td>fungicide</td><td>Application of fungicides during the crop cycle</td><td>yom (yes), nom (no)</td></tr><tr><td></td><td>foliar</td><td>Application of foliar fertilizer during the crop cycle</td><td>yfung (yes), nfung (no)</td></tr><tr><td></td><td>alternative</td><td>Application of organic/natural/alternative phytosanitary products during the crop cycle</td><td>yalt (yes), nalt (no)</td></tr><tr><td></td><td>othermol</td><td>Application of another chemical molecule (e.g. organophosphate) during the crop cycle</td><td>yom (yes), nom (no)</td></tr><tr><td></td><td>lasttreatpy</td><td>Recent application of pyrethrinoids</td><td>ylpy (yes), nlpy (no)</td></tr><tr><td></td><td>lasttreatne</td><td>Recent application of neonicotinoids</td><td>ylne (yes), nlne (no)</td></tr><tr><td></td><td>lasttreatav</td><td>Recent application of avermectin</td><td>ylav (yes), nlav (no)</td></tr><tr><td></td><td>lasttreatfung</td><td>Recent application of fungicides</td><td>ylo (yes), nlo (no)</td></tr><tr><td></td><td>lasttreatfoliar</td><td>Recent application of foliar fertilizer</td><td>ylfung (yes), nlfung (no)</td></tr><tr><td></td><td>lasttreatalt</td><td>Recent application of organic/natural/alternative crop protection products</td><td>ylfol (yes), nlfol (no)</td></tr><tr><td></td><td>nbchemicalmol</td><td>Number of different active substances applied throughout the crop cycle</td><td>ynalt (yes), nlalt (no)</td></tr></tbody></table>
<table><caption>**Table 1 (continued)**</caption><thead><tr><th>Variable group</th><th>Variable name</th><th>Definition</th><th>Values/Levels</th></tr></thead><tbody><tr><td rowspan="2"></td><td>tficycle</td><td>Treatment frequency index (TFI) throughout the cucurbit cycle, calculated as the ratio of the dose applied by the farmer on the reference dose of the product divided by theoretical number of treatments throughout the crop cycle</td><td>Number between 0 and 160</td></tr><tr><td>tfismp</td><td>TFI until the visit day, calculated as the dose applied by the farmer divided by the reference dose of the product, divided by the number of treatments conducted before our visit</td><td>Number between 0 and 125</td></tr><tr><td rowspan="4">Perceptions of the farmer</td><td>mixprod</td><td>Application of several phytosanitary products combined</td><td>ymixprod (yes), nmixprod (no)</td></tr><tr><td>mixinsecticides</td><td>Application of several insecticides combined</td><td>ymixinsect (yes), nmixinsect (no)</td></tr><tr><td>managinfestfruit</td><td>Infested fruit management strategy</td><td>abandon (left on the plot), exit (exported from the plot), dep (either of the previous strategy depending on farmer's other constraints)</td></tr><tr><td>importanceff</td><td>Extente of importance of fruit fly damage</td><td>Integer: 0 (never seen flies or maggots), 1 (already seen but no link with damage), 2 (knowledge on the biological cycle)</td></tr><tr><td></td><td>knowledgeff</td><td>Level of knowledge on fruit flies</td><td></td></tr></tbody></table>

and quarterly sampling period (T1: February to April, T2: May to July, T3: August to September) to characterize seasonal variations. We characterized the environmental conditions by defining a large-scale dominant landscape and a plot-scale environment. We also noted the presence of certain plants in the vicinity of the plot as well as the vegetation composition along its 4 edges. We characterized the farmers' crop management and agricultural practices by recording: the farm and plot areas, crop associations, cucurbit crop age, color and skin texture of the cucurbit varieties, planting density, weed cover, previous crops, implementation of crop rotation or not, irrigation system, fertilization type, crop protection products used, number of different active substances used throughout the crop cycle, their application frequency and whether they were applied individually or combined, the last applied product and the last treatment date, harvest frequency, number of previous harvests, and finally the damaged fruit management strategy. We characterized each sampled fruit on the basis of the cucurbit species (cucumber or zucchini), fruit weight, shape and total number of lesions on the fruit surface. In addition to these biotic and abiotic factors, we recorded farmers' perceptions and knowledge on fruit flies and their damage.

## Fruit collection and incubation

Among the infested fruits monitored on each plot, a minimum of 10 fruit at different maturity stages (when available)—which were the most likely to host Tephritid eggs and/or larvae—were collected. These fruits were then incubated in the laboratory according to the methods described by Vayssières et al. (2007) and placed in individual transparent plastic boxes (Zenith Plastic®) containing a 2 cm layer of moistened sterilised sand as a pupation substrate, with holes to allow excess water evacuation. There were also holes in the lid to facilitate air flow. These boxes were maintained at room temperature (at the Ecole Supérieure d'Agriculture, Yamoussoukro, Côte d'Ivoire) under rotating fan. The sand under the fruit was sifted twice a week for 15 days to collect and count pupae, and the sand was the placed back in the box and rehumidified. All pupae from the same collection session (1 fruit at 1 date) were placed on a piece of laminated and slightly humidified cardboard in a round transparent plastic box (Zenith Plastic®) with pierced lid and they were left in the same room conditions until emergence. Each box was checked daily and rehumidified to avoid pupae desiccation. Emerged flies and parasitoids were released in a cage (47.5 × 47.5 × 93cm, Model 4M4590, Bugdorm®) and immediately captured in a vial filled with 90° ethanol. They were counted and identified (sex and species) by eye,
<table><caption>**Table 2** Group, name and values of all studied variables at the fruit scale</caption><thead><tr><th>Variable group</th><th>Variable name</th><th>Definition</th><th>Values/Levels</th></tr></thead><tbody><tr><td rowspan="4">Response variables</td><td>nbpupaefruit</td><td>Number of pupae collected from each sampled fruit</td><td>Integer between 0 and 223</td></tr><tr><td>nbspc</td><td>Number of different fly species/sampled fruit (species richness)</td><td>Integer from 1 to 3</td></tr><tr><td>raX</td><td>Proportion of species X individual flies emerged from one fruit relative to the total number of flies emerged from the same fruit (relative abundance)</td><td>Number from 0 to 1</td></tr><tr><td>nbXtot</td><td>Number of species X individual flies emerged from each sampled fruit</td><td>Integer between 0 and 89</td></tr><tr><td rowspan="4">Fruit characteristics</td><td>cucurbit</td><td>Sampled cucurbit species</td><td>cusat (Cucumis sativus), cupep (Cucurbita pepo)</td></tr><tr><td>weight</td><td>Fruit weight</td><td>Number between 0.07 and 0.904 kg</td></tr><tr><td>shape_bin</td><td>Fruit shape</td><td>straight, curved</td></tr><tr><td>nblesionstot</td><td>Number of lesions due to flies and/or larvae on the fruit surface</td><td>Integer between 0 and 15</td></tr></tbody></table>

or under a stereomicroscope (Wild M8, Leica®) when in doubt, and using identification keys (Ekesi & Billah 2007; Virgilio et al. 2014; Ekesi et al., 2016b; Kirk-Spriggs and Sinclair (eds.), 2021). They were stored in 90° ethanol (1 vial/fruit) in a fridge. Some individual flies and parasitoids of different morphotypes were set aside for further barcoding analysis to confirm their identification (Fig. 13, Supplementary Material). Finally, remaining rotten or relatively well conserved fruit were dissected to assess the extent of internal damage and potential remaining larvae and pupae.

## Data analysis

All statistical analyses were performed using the R Studio 4.2.2 software.

Our response variables to be explained were the: (i) infestation rate (proportion of infested fruit/plot); (ii) infestation level (number of pupae/sampled fruit); (iii) species richness at the plot scale (number of fruit fly species/plot); (iv) species richness/fruit (number of fruit fly species/fruit); (v) species abundance/plot (mean number of fly individuals of each species in 10 sampled fruits/plot); (vi) species abundance/fruit (number of fly individuals of each species/fruit); (vii) relative abundance/plot (proportion of each fruit fly species per plot); and (viii) relative abundance/fruit (proportion of each fruit fly species/fruit).

Factor analysis of mixed data (FAMD) ('FactoMineR' package) findings were used to select variables that were the most representative of the data and the nearest (by Euclidian distance) from our response variables (infestation rate, species richness and relative abundance) on the principal component plane to which they respectively contributed the most.

A generalised linear mixed model (GLM) with a binomial distribution and corrected for overdispersion ('dispmod' package) was implemented to explain the infestation rate.

A generalised linear mixed model (GLMM) was then implemented to explain the infestation rate with the plot number as random effect. The negative binomial distribution was used to correct overdispersion and the hurdle method ('GLMM adaptive' package) was also used to take sampled fruit that were ultimately not infested into account. There was hence a model to explain the presence of pupae and another to explain the number of pupae in infested fruit.

A GLM with Poisson distribution ('stats' package) was implemented to explain the species richness at the plot scale.

To explain the species richness at the fruit scale, a hurdle GLMM model ('GLMM adaptive' package) was applied on the number of species/fruit tested against 4 fruit characteristics: weight, shape, cucurbit species and number of lesions.

To explore species preferences at the plot scale, candidate variables selected by FAMD were tested against an ordination of the species abundance of the 4 major species via redundancy analysis (RDA) ('vegan' package).

To explore species preferences at the fruit scale, we performed 3 separated hurdle GLMM ('GLMM adaptive' package) on the number of individuals/fruit (species abundances) explained by 4 fruit characteristics: weight, shape, cucurbit species and number of lesions.

To explain competition at the plot scale, candidate variables selected by a FAMD were tested against an ordination of the relative abundance of the 4 major species via RDA ('vegan' package).

To explain interspecific competition at the fruit scale, a GLMM was implemented to explain the number of individuals of the predominant species in our samples by the number of individuals of each of the other species, the cucurbit species and fruit weight, as to highlight the mechanisms of interspecific competition for oviposition choice. The negative binomial distribution was used to correct overdispersion ('lme4' package).

For each GLM or GLMM, a step-by-step selection based on the Akaike information criterion (AIC) was implemented to
a

<figure><img src="image_5.png" /></figure>

b

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

c

<figure><img src="image_7.png" /><figcaption>**Fig. 1** Analysis of the impact of the candidate variables on the infestation rate: (a) Correlation circle plot of quantitative variables on the first two dimensions (supplementary variables not shown for readability); (b) Correlation chart of all variables on the first two dimensions (supplementary variables not shown for readability); (c) Model</figcaption></figure>

retain the most relevant variables to explain the response vari-
ables. Post-hoc tests ('statix' and 'multcomp' packages) were
performed to discriminate which modalities of a qualitative
variable were responsible for its effect on the response variable.

Finally, we performed T-tests ('stats' package) to determine
the dependence between the infestation rate, the farmers' per-
ception regarding the fruit fly pest status, the extent of knowl-
edge on fruit flies and their infested fruits management strategy.

estimates of the effects of the 8 explanatory variables and their cat-
ories determined by AIC selection to explain the infestation rate, as
reported above the dots, followed by significance code (Wald p-val-
ues), while horizontal bars represent the standard error

Results

Which factors affected the plot infestation?

90% of the monitored cucurbit plots were infested by fruit
flies with an infestation rate ranging from 1 to 100% and a
mean of 31 ± 3% of infested fruit. Of the 58 variables used
in the FAMD tested against the infestation rate, 37 were
a

Graph of the quantitative variables

b

Graph of the variables

c

<caption>**Fig. 2** Analysis of the impact of the candidate variables on the fruit infestation level: (a) Correlation circle plot of quantitative variables on the first two dimensions; (b) Correlation chart of all variables on the first two dimensions; (c) Model estimates of the effects of the 4</caption>

considered supplementary because they were poorly repre-
sented on the principal component plane of the infestation
rate. Among the remaining 21 variables, 14 were selected
for the GLM analysis because they were not correlated
with respect to each other and they were the nearest to the

infestation rate on the principal component plane (Fig. 1a,
1b). Based on the minimal AIC, the final GLM retained the
8 following explanatory variables: weed cover, previous
crop, city, crop age, cucurbit skin texture of the cucurbit,
recent application of pyrethrinoids, avermectin or alternative

explanatory variables and their categories to explain the infestation
level, as reported above the dots and followed by the significance
code (Wald p-values), while horizontal bars represent the standard
error

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

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

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>
<figure><img src="image_12.png" /><figcaption>b</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>c</figcaption></figure>

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

<figure><img src="image_15.png" /><figcaption>**Fig. 3** Effects of fruit characteristics on its infestation level: (a) Proportion of infested fruit according to the cucurbit species, shape and number of lesions; (b) Boxplots of fruit weights according to their</figcaption></figure>

product (Fig. 1c). The infestation rate increased with the extent of weed cover between plant rows (p = 0.01). This infestation rate was lower in Abidjan (in the south) than in norther and central areas (p < 0.001). The infestation rate was lower in plots where the cucurbit variety had a hard skin rather than soft one (p = 0.02). The infestation rate was also lower in plots where avermectin was their most recent treatment before sampling (p = 0.02).

**Which factors affected the fruit infestation level?**

55% of the sampled fruit were infested by fruit flies. The number of pupae/fruit ranged from 1 to 223 with a mean of 16 ± 1. FAMD revealed that the number of pupae/fruit

infestation status; (c) Number of pupae collected from fruit according to the cucurbit species; (d) Number of pupae collected from fruit according to the number of visible lesions

was correlated with the number of fruit lesions but anti-correlated with the fruit weight (Fig. 2a), and that these two variables were the nearest to pupae number (Fig. 2b). The following GLMM aimed at specifying and quantifying these between-variables relationships. Pupae collected from 72% of the sampled zucchinis, whereas less than half of the sampled cucumbers did not render any pupae (Fig. 3a). The number of pupae collected from zucchinis was higher than from cucumbers (Fig. 2c, 3c). Smaller, i.e. immature, fruit were more likely to render pupae compared to bigger, i.e. more mature, fruit (Fig. 2c, 3b). Curved fruit were more likely to render pupae compared to straight fruit (Fig. 2c, 3a). The proportion of infested fruit decreased with the number of lesions on the fruit surface (Fig. 2c, 3a). However, the
<figure><img src="image_16.png" /><figcaption>**Fig. 4** Analysis of the impact of the candidate variables on the species richness at the plot scale: (a) Correlation circle plot of quantitative variables on the first two dimensions; (b) Correlation chart of all variables on the first two dimensions; (c) Model estimates of the</figcaption></figure>

number of pupae/fruit increased with the number of lesions (Fig. 2c, 3d).

## Which factors affected the species richness per plot?

Five different fruit fly species from the sampled fruits were identified, with the main 3 being *Zeugodacus cucurbitae*, *Dacus ciliatus* and *Dacus bivittatus*. A few *Dacus vertebatus* and *Dacus punctatifrons* individuals were also found in some plots. 34% of the infested plots were infested by only one of these species (67% with *D. ciliatus*, 25% with *Z. cucurbitae* and 8% with *D. bivittatus*), 34% were infested by 2 species, 22% by 3 species, 8% by 4 species and only 1.4% were infested by all 5 of these species.

effects of the 4 explanatory variables and their categories to explain the species richness at the plot scale, reported above the dots and followed by the significance code (Wald p-values), while horizontal bars represent the standard error

Here we aimed to explain the number of species occurring on each plot. 18 candidate explanatory variables were selected on the basis of FAMD (Fig. 4a). Based on the minimal AIC, the final GLM retained 4 variables, including 2 significant ones (Fig. 4c). We found that the number of species/plot increased with the number of days since the last treatment (Fig. 4c, 5a) whereas it decreased with the TFI (Fig. 4c, 5b), thereby indicating that the more often the farmers sprayed their plot, the less species were able to thrive. Overall, we found that the city and period, presence of cucurbits, leafy crops or human infrastructure on one edge, the large-scale dominant landscape and plot-scale environment, or weed cover and irrigation, recent use of avermectin, the cucurbit skin texture, the previous crop

<figure><img src="image_17.png" /></figure>
<figure><img src="image_18.png" /><figcaption>**Fig. 5** Number of fruit fly species/plot according to: (a) the number of days since last treatment and (b) the total treatment frequency index (TFI) throughout the crop cycle</figcaption></figure>

and the infestation rate had no impact on the number of species/plot. Notably, a most infested plot did not display the most species.

## Which factors affected the species richness per fruit?

Flies emerged from 47% of the collected fruits. 77% of them were infested by one fly species (57% by *D. ciliatus*, 34% by *Z. cucurbitae*, 7% by *D. bivittatus*, 1% by *D. verteb-ratus* and 1% by *D. punctatifrons*), 22% were co-infested by 2 species and 1% were co-infested by 3 species. With the same approach as that used for the infestation level (FAMD Fig. 6a, 6b, followed by a hurdle GLMM, Fig. 6c), we found that zucchinis were more likely to render flies than cucumbers (Fig. 6c, 7a), as well as smaller fruit rather than mature ones (Fig. 6c, 7b), which was consist-ent with our previous results on infestation levels (number of pupae/fruit). Curved fruits were more co-infested than straight fruits (Fig. 6c, 7c). Fruit with more lesions were more likely to be co-infested (Fig. 6c, 7c).

## Which factors affected the species abundance per plot?

*Dacus ciliatus* was present on 76% of the monitored plots, with a mean species abundance of 30 ± 5 individuals/10 sampled fruits. *Zeugodacus cucurbitae* was present on 61% of the monitored plots, with a mean species abun-dance of 24 ± 6 individuals/10 sampled fruits. *Dacus bivittatus* was present on 36% of the monitored plots, with a mean species abundance of 19 ± 8 individuals/10 sam-pled fruits. Minor species (i.e. *Dacus vertebratus* and *D.*

*punctatifrons*) were present on 19% of the monitored plots, with a mean species abundance of 1 ± 0.3 individuals/10 sampled fruits. 30 non-correlated explanatory candidate variables were selected on the basis of the FAMD findings as being the nearest to at least one of the species abun-dance of the 3 main species. The following RDA analysis revealed 5 variables with significant effects on at least one species abundance (Table 3). The number of *Z. cucurbitae* individuals/sampled fruit increased with the infestation rate (p < 0.001) (Fig. 8) and the weed cover (p = 0.016) (Fig. 9a), and was higher at northern sites (Korhogo) than at southern sites (Abidjan) (p = 0.035) (Fig. 9b). The num-ber of *D. bivittatus* individuals/per sampled fruit increased with the number of days since last treatment (p = 0.001) and with the TFI at the sampling date (p = 0.008) (Fig. 8).

## Which factors affect the species abundance per fruit?

*Dacus ciliatus* was present in 29% of the sampled fruits with a mean species abundance of 8 ± 1 individuals/ infested fruit. *Zeugodacus cucurbitae* was present in 20% of the sampled fruits with a mean species abundance of 8 ± 1 individuals per/infested fruit. *Dacus bivittatus* was present in 6% of the sampled fruits with a mean specific abundance of 9 ± 2 individuals/fruits. Minor species (i.e. *Dacus vertebratus* and *D. punctatifrons*) were present on 3% of the sampled fruits with a mean species abundance of 4 ± 1 individuals per/infested fruit. A hurdle GLMM analysis revealed that more zucchinis hosted *Dacus ciliatus* than cucumbers (p < 0.001), as was also the case for curved fruits as compared to straight ones (p < 0.001) (Fig. 10a),
<figure><img src="image_19.png" /><figcaption>**Fig. 6** Analysis of the impact of the candidate variables on the species richness at the fruit scale: (a) Correlation circle plot of quantitative variables on the first two dimensions; (b) Correlation chart of all variables on the first two dimensions; (c) Model estimates of the</figcaption></figure>

effects of the 4 explanatory variables and their categories to explain the species richness at the fruit scale, reported above the dots and followed by the significance code (Wald p-values), while horizontal bars represent the standard error

whereas neither the cucurbit species nor the shape had an effect on the presence of the two other main species (Fig. 10bc 10e). More small fruits than mature fruits hosted *Dacus ciliatus* (p = 0.005) (Fig. 10a) and *Zeugodacus cucurbitae* (p = 0.004) (Fig. 10c). The more lesions on the fruit, the more likely they were to host *Dacus bivittatus* (p = 0.003) (Fig. 10e), while more individuals of this species emerged from small fruits than from mature fruits (p = 0.008) (Fig. 10f). This indicated that the fruit weight was the main interspecific competition factor and that *Dacus bivittatus* was an opportunistic species that opted to lay its eggs in already infested fruits. Indeed, in our samplings, this species was present in 8% of monospecific-infested fruit, 22% of bispecific-infested fruit and 100% of trispecific-infested fruit.

## Which factors affected the relative abundance per plot?

The *Dacus ciliatus* mean relative abundance on infested plots was 0.52 ± 0.05 and it was the major species on 54% of the infested plots. The *Zeugodacus cucurbitae* mean relative abundance was 0.31 ± 0.04 and it was the major species on 25% of the infested plots. The *Dacus bivittatus* mean relative abundance was 0.15 ± 0.03 and it was the major species on 20% of the infested plots. The mean relative abundance of the minor species (*Dacus vertebratus* and *D. punctatifrons*) was 0.02 ± 0.07 and *D. punctatifrons* was the major species on 1% of the infested plots. 20 non-correlated explanatory candidate variables were selected on the basis of FAMD findings as the nearest to at least one of the 3 main fruit fly species' relative abundance. The following RDA analysis
a

<figure><img src="image_20.png" /></figure>

b

<figure><img src="image_21.png" /></figure>

c

<figure><img src="image_22.png" /><figcaption>**Fig. 7** (a) Proportion of sampled fruit of the two cucurbit species from which flies emerged; (b) Fruit weights according to their infestation status; (c) Number of fly species that emerge from fruit according to the fruit shape and the number of surface lesions</figcaption></figure>
<table><caption>**Table 3** Goodness-of-fit of the 30 variables tested against an ordination of the species abundance of the 4 fruit fly species (10⁵ permutations)</caption><thead><tr><th>Variable group</th><th>Variable name</th><th>R<sup>2</sup></th><th>P</th></tr></thead><tbody><tr><td rowspan="2"><strong>Response variables</strong></td><td><strong>Infestrate</strong></td><td><strong>0.353</strong></td><td><strong>&lt;0.001</strong></td></tr><tr><td><strong>City</strong></td><td><strong>0.094</strong></td><td><strong>0.035</strong></td></tr><tr><td rowspan="10"><strong>Landscape/environment</strong></td><td>Domlandscape</td><td>0.087</td><td>0.232</td></tr><tr><td>Plotenv</td><td>0.102</td><td>0.112</td></tr><tr><td>Divers</td><td>0.031</td><td>0.144</td></tr><tr><td>Serviceplant</td><td>0.003</td><td>0.792</td></tr><tr><td>Cucurbitedge</td><td>0.011</td><td>0.626</td></tr><tr><td>Leafyvegedge</td><td>0.002</td><td>0.934</td></tr><tr><td>Potsechostedge</td><td>0.029</td><td>0.161</td></tr><tr><td>Refugeplantedge</td><td>0.011</td><td>0.587</td></tr><tr><td>Soiledge</td><td>0.011</td><td>0.512</td></tr><tr><td>Artificialedge</td><td>0.029</td><td>0.163</td></tr><tr><td rowspan="13"><strong>Crop management and agricultural practices</strong></td><td>Asso</td><td>0.012</td><td>0.527</td></tr><tr><td>color</td><td>0.016</td><td>0.797</td></tr><tr><td>surfcucurbit</td><td>0.004</td><td>0.846</td></tr><tr><td><strong>weedcover</strong></td><td><strong>0.083</strong></td><td><strong>0.016</strong></td></tr><tr><td>previouscrop</td><td>0.048</td><td>0.450</td></tr><tr><td>rotation</td><td>0.024</td><td>0.227</td></tr><tr><td>irrigation</td><td>0.030</td><td>0.765</td></tr><tr><td>ferti</td><td>0.039</td><td>0.310</td></tr><tr><td>treatfreq</td><td>0.009</td><td>0.651</td></tr><tr><td><strong>lasttreat</strong></td><td><strong>0.469</strong></td><td><strong>0.001</strong></td></tr><tr><td>harvestfreq</td><td>0.080</td><td>0.070</td></tr><tr><td>lastharvest</td><td>0.013</td><td>0.663</td></tr><tr><td>harvestnb</td><td>0.054</td><td>0.180</td></tr><tr><td>av</td><td>0.016</td><td>0.395</td></tr><tr><td>lasttreatfung</td><td>0.008</td><td>0.563</td></tr><tr><td><strong>tfismp</strong></td><td><strong>0.329</strong></td><td><strong>0.008</strong></td></tr><tr><td>mixprod</td><td>0.010</td><td>0.626</td></tr><tr><td>managinfestfruit</td><td>0.063</td><td>0.101</td></tr></tbody></table>

revealed 4 variables with significant effects on the relative abundance of at least one species (Table 4).

The relative abundance of *Z. cucurbitae* increased with the infestation rate (p < 0.001) (Fig. 11), indicating that the more infested fruits there were on the plot, the more likely it was that this species was dominant. Subsequently, it was found that *Dacus bivittatus* was more dominant during August to October period (p < 0.01). This species was also most likely to dominate in larger cucurbit plots (p < 0.05) and as the number of days since the last treatment increased (p < 0.01) (Table 4, Fig. 11).

## Which factors affected the relative abundance per fruit?

To gain further insights into the mechanisms of the previous interspecific competition phenomenon, we tried to explain the number of *Dacus ciliatus* emerged from infested fruit on the basis of the respective numbers of *Zeugodacus cucurbitae* and *Dacus bivittatus* individuals and the fruit characteristics, including the interaction between the number of individuals of these two species and the fruit weight. Apart from the individual preference of *Dacus ciliatus* for zucchini, small and curved fruits (Fig. 12a), as indicated above in part 3.f., we found that *Dacus ciliatus* was more likely to emerge from smaller fruit when *Zeugodacus cucurbitae* was absent rather than present (Fig. 12b).

## Farmers’ perception of fruit flies

Out of the 47 farmers interviewed, 11 (23%) declared that they had never seen a cucurbit-infesting Tephritid (pictures shown during the survey) nor were they aware that this insect was responsible for their damaged cucurbit crops. 31 (66%) declared that they had already seen flies or maggots in damaged fruit. 5 (11%) were able to explain the link between fruit damage and the fruit fly life cycle. Despite this knowledge heterogeneity, they had a clear idea of the importance of this pest in their plots according to the infestation rate measured during the visit (T-test, p < 0.001, Fig. 14 Supplementary Material).

Out of the 62 monitored plots, infested fruit were abandoned on 39 of them (63%), exported for 15 of them (24%) and one or the other management strategy depending on current constraints for 8 plots (13%). The management strategy did not depend on the infestation rate nor the farmers’ level of awareness regarding fruit flies.

## Discussion

This study on market cucurbit cropping systems in Côte d’Ivoire was designed to assess the biotic and abiotic factors involved in the damage caused by fruit flies, while also investigating their diversity. 90% of monitored plots were infested by fruit flies, thereby confirming its status as pest of major concern. The infestation rate ranged from 1.9 to 100% (mean: 30.5 ± 3.2%), which was in line with the situation in the cucurbit plots monitored by Mokam et al. (2018) in Cameroon, where a mean infestation rate of 36.7% was reported. Five species were identified, including *Dacus ciliatus*, *Dacus bivittatus*, *Zeugodacus cucurbitae*, *Dacus punctatifrons* and
<figure><img src="image_23.png" /><figcaption>**Fig. 8** RDA biplot of the monitored plots (black dots) and species abundance of the 4 main species (black arrows) tested against the 30 non-correlated explanatory candidate variables (grey arrows)</figcaption></figure>

<figure><img src="image_24.png" /><figcaption>**Fig. 9** Mean number of *Zeugodacus cucurbitae* individuals/per 10 sampled fruits/plot according to (a) the weed cover level and (b) the sampling location</figcaption></figure>
<figure><img src="image_25.png" /><figcaption>**Fig. 10** Model estimates of the effects of the 4 explanatory variables and their categories to explain the species abundances at the fruit scale in terms of presence of (a) *Dacus ciliatus*, (b) *Dacus ciliatus* spp. and (c) *Dacus bivittatus*; and number of (d) *Dacus ciliatus*, (e) *Dacus bivittatus* and (f) *Dacus bivittatus* individuals/fruit, reported above the dots and followed by the significance code (Wald p-values), while horizontal bars represent the standard error</figcaption></figure>

*Dacus vertebratus*, all of which had been previously reported in Côte d’Ivoire or other African countries (Ekesi and Billah 2007; De Meyer et al. 2013; Virgilio et al. 2014; Mokam et al. 2018; Kambura et al. 2018; N’Depo et al. 2010; Zida et al. 2020; Amevoin et al. 2021). *Dacus ciliatus* was found to be the predominant species on most of the plots, followed by the invasive species *Zeugodacus cucurbitae*. In Kenya, *Z. cucurbitae* was the dominant species found on indigenous *Dacus spp* (Kambura et al. 2018), which contrasted with the situation in Cameroon where *D. bivittatus* was found to be dominant (Mokam et al. 2014, 2018). In their field guide, Ekesi and Billah (2007) considered that the central region of Côte d’Ivoire (Yamoussoukro and Bouaké) had a high potential area for *Z. cucurbitae* infestations, contrary to the other parts of the country which these authors considered had a low potential. However, in our sampling campaign, we collected more *Z. cucurbitae* individuals, and noted a

higher relative abundance northern Côte d’Ivoire, in line with previous findings in the same area (Tuo et al. 2018).

Despite its predominance in our sampling campaign, *Dacus ciliatus* did not show preference for any particular environmental or agricultural practices at the plot scale, contrary to the two other main species (details below), but it was more likely to be dominant when the number of days since the last harvest increased. Previous inventories have shown that this African-native species is widely distributed throughout Africa (Ekesi and Billah 2007; De Meyer et al. 2013). Moreover, it was qualified as being ubiquitous in all climatic zones (Vayssières 1999) and even appears to thrive in drier regions of Africa (Tanga and Rwomushana 2016), thus suggesting it is a major competitor of *Zeugodacus cucurbitae* (De Meyer et al. 2013). Indeed, we noted inter-specific competition between these two species at the fruit scale on the basis of an interaction effect between the fruit

<figure><img src="image_26.png" /></figure>
<table><caption>**Table 4** Goodness-of-fit of the 20 variables tested against an ordination of the relative abundance of the 4 fruit fly species (10⁵ permutations)</caption><thead><tr><th>Variable group</th><th>Variable name</th><th>R<sup>2</sup></th><th>p</th></tr></thead><tbody><tr><td rowspan="2"><strong>Response variables</strong></td><td><strong>infestrate</strong></td><td><strong>0.325</strong></td><td><strong>&lt;0.001</strong></td></tr><tr><td>city</td><td>0.0693</td><td>0.199</td></tr><tr><td rowspan="3"><strong>Global/climatic</strong></td><td><strong>quarter</strong></td><td><strong>0.137</strong></td><td><strong>0.001</strong></td></tr><tr><td>potsechostedge</td><td>0.0121</td><td>0.489</td></tr><tr><td>artificialedge</td><td>0.0326</td><td>0.134</td></tr><tr><td rowspan="10"><strong>Crop management and agricultural practices</strong></td><td><strong>surfcucurbit</strong></td><td><strong>0.096</strong></td><td><strong>0.048</strong></td></tr><tr><td>previouscrop</td><td>0.0468</td><td>0.474</td></tr><tr><td>irrigation</td><td>0.0601</td><td>0.289</td></tr><tr><td>treatfreqdays</td><td>0.0655</td><td>0.136</td></tr><tr><td><strong>lasttreat</strong></td><td><strong>0.152</strong></td><td><strong>0.01</strong></td></tr><tr><td>harvestfreq</td><td>0.0514</td><td>0.171</td></tr><tr><td>lastharvest</td><td>0.081</td><td>0.087</td></tr><tr><td>av</td><td>0.0043</td><td>0.772</td></tr><tr><td>fungicide</td><td>0.0215</td><td>0.269</td></tr><tr><td>lasttreatne</td><td>0.0091</td><td>0.571</td></tr><tr><td>lasttreatfoliar</td><td>0.0104</td><td>0.577</td></tr><tr><td>nbchemicalmol</td><td>0.0435</td><td>0.271</td></tr><tr><td>tfismp</td><td>0.0172</td><td>0.610</td></tr><tr><td>mixinsecticides</td><td>0.004</td><td>0.789</td></tr><tr><td>managinfestfruit</td><td>0.0268</td><td>0.524</td></tr></tbody></table>

weight and the number of *Z. cucurbitae* individuals/fruit as compared to the number of *Dacus ciliatus* individuals/fruit. According to Jacquard et al. (2012), *Z. cucurbitae* is less affected by interspecific larval competition because the majority of its larvae can develop small light pupae, even in fruit with a high larvae density, in line with our lab observations. However, we also noted the presence of many small *Dacus ciliatus* individuals in highly infested fruit. Before direct larval competition for fruit resources and in order to avoid or minimize this phenomenon, this difference might be explained in terms of preferred times of the day to lay eggs of the different fruit fly species, as well as their larval development times (Ekesi et al. 2016a, b). Indeed, we often detected *Dacus bivittatus* on plots in the late afternoon, contrary to the two other species that were generally detected at the hottest times of the day, confirming the different activities of species according to their circadian rhythm (Vayssières 1999).

Overall, we found more pupae in zucchinis, and in small and curved sampled fruits. There were more species in curved fruit and in those with a high number of lesions. We detected more *D. ciliatus* individuals in zucchinis and curved fruits, more *D. bivittatus* individuals in fruits with more lesions, and more of all 3 of the main fruit fly species in smaller fruit, as also reported by Mokam et al. (2018) who found that females laid eggs preferentially in small fruit

(<100 g). Kambura et al. (2018) also reported more fruit fly emergence on zucchinis and fewer pupae on cucumbers. Zucchini and small fruit were hence more attractive and/or suitable for fly development. The presence of *Dacus bivittatus* was thus more likely in fruits with a high number of lesions, suggesting that this species prefers to lay eggs in already infested fruits and pierce new oviposition holes rather than using pre-existing ones. The latter resulted in more oviposition holes on the fruit, consistent with the observations of Fomekong et al. (2008) and also confirming that *Dacus bivittatus* is an opportunistic species (Aluja and Mangan 2008). Moreover, Mokam et al. (2018) found that a majority of monoinfested fruits mainly hosted *Dacus ciliatus*, and all pluriinfested fruits (2 or 3 species) hosted *Dacus bivittatus*. The higher attractiveness of curved fruit in terms of fly quantity and diversity could be explained by the fact that females could preferentially lay their eggs in the curvature of the fruit out of sight of predators and other females, while not being hampered by wind, for instance, since egg laying can last up to 20–40 min (Vayssières 1999). However, the causal link between fruit curvature and fruit fly attack is not obvious, as farmers often complained that their fruits were curved because of fruit fly attacks. Finally, according to Dhillon et al. (2005), and confirmed by Mokam et al. (2018), females prefer soft-skinned fruits, which we also noted at the plot scale because the most infested plots tend to be cropped with hard skin cucurbit varieties. At the fruit scale, we collected more pupae in smaller fruits because they generally had a softer skin that would be easier to pierce than that of more mature fruits.

We found more *Zeugodacus cucurbitae* individuals in northern and more infested plots and with greater weed cover, and also in smaller fruits in competition with *D. ciliatus* (see above). This first invasive species thus appeared to be responsible for the high infestation rates found in northern Côte d'Ivoire due to the fact that they preferentially infested healthy fruit to avoid competition with *Dacus ciliatus*. In these northern areas, *Z. cucurbitae* could also benefit from weed cover, serving as an oasis of freshness to thrive and dominate, as reported for *Ceratitis sp.* and *Bacrocera zonata* in Egyptian orchards with various extents of weed cover (Awadalla et al. 2019). Moreover, we observed both higher weed cover and *Z. cucurbitae* infestations in the rainy season during second quarter (May to July). Indeed, high humidity is favourable for both rapid weed growth and fly development, whereas Tanga and Rwomushana (2016) found that *Dacus ciliatus* thrived better in arid climatic conditions.

We found more *Dacus bivittatus* individuals but lower species richness when the TFI increased and in the most recently treated plots, suggesting that the proliferation of this species was less hampered by frequent and intensive crop protection practices than the other species. This could
<figure><img src="image_27.png" /><figcaption>**Fig. 11** RDA biplot of the monitored plots (black dots) and relative abundance of the 4 main species (black arrows) tested against the 20 non-correlated explanatory candidate variables (grey arrows)</figcaption></figure>

be due to their respective circadian rhythms, as discussed above, as *Dacus bivittatus* seems to invade plots in the evening, thus avoiding morning control treatments. Moreover, we noted lower infestation rates in plots recently treated with avermectin, suggesting that the nature of the pesticide active ingredients could reduce infestations through a distance repellent and/or contact oviposition deterrent effects. Indeed, some studies have highlighted the differential toxicity of pyrethrinoids, organophosphate or avermectin depending on fruit fly species and host plants, while also showing an effect on fruit damage (Elsayed et al. 2022; Susanto et al. 2024; Gyawali et al. 2023; Li et al. 2024). In Cameroon, Mokam et al. (2014, 2018) showed that the landscape (rural vs. urban) contributed more to the cucurbit-Tephritid species richness

than the agroecological zone (southern plateau vs. western highlands) or the rainfall regime (unimodal vs. bimodal), thereby indicating that this variable is driven by proximity rather than climatic effects, as highlighted in our study at an even finer scale through agricultural practices at the plot scale.

In conclusion, our study revealed differential preferences of fruit fly species for some characteristics of the field plot environment (location and weed cover), management conditions (treatment frequency) and/or the fruit (weight, shape, already infested). This showed that the preferential conditions and competition strategies differed between the fruit fly species, hence explaining the extent of damage and fly diversity observed in the field. This confirmed the importance of clearly identifying the pest species and gaining insights

<figure><img src="image_28.png" /></figure>
a

<figure><img src="image_29.png" /><figcaption>**Fig. 12** (a) Model estimates of the effects of the number of *Zeugodacus cucurbitae* and *Dacus bivittatus* individuals and fruit characteristics on the presence (left) and number (right) of *Dacus ciliatus* individuals/fruit, reported above the dots and followed by the significance code (Wald p-values), while horizontal bars represent the standard</figcaption></figure>

into their preferences so as to effectively adapt the control
strategy, with prophylaxis being essential, and according
to the complexity and uncertainty related to multi-specific
infestations. Based on our results, we recommend efficient
weed control to curb *Zeugodacus cucurbitae* infestations,
along with the removal of infested fruits from the cropfield
to avoid infestation by other opportunistic species. Insect-
proof netting would also prevent females from coming in
contact with fruit and laying their eggs inside.

**Supplementary Information** The online version contains supplementary material available at https://doi.org/10.1007/s42690-025-01622-4.

**Acknowledgements** We acknowledge the financial support provided by the European ommision through the Development of Smart Innovation through Research in Agriculture (DeSIRA) Initiative, in particular the MARIGO project (Maraîchage agro-écologique péri-urbain en Côte d'Ivoire, grant number FOOD/2020/419-988). We also acknowledge the UMR Sciences Agronomiques et Procédés de Transformation

error; (b) Presence (dark dots) or absence (grey dots) of *Dacus ciliatus* in fruit already infested by *Zeugodacus cucurbitae* or not (box-plot left), according to the number of individuals of the latter species (log-scale, ellipse chart right) and the fruit weight

(Institut National Polytechnique Felix Houphouët-Boigny, Yamous-soukro, Côte d'Ivoire) for hosting the research. We finally acknowledge all of the field agents and managers of the project partner organizations for their time and resources for the field monitoring, as well as all the farmers for welcoming us on their filed plots and for sharing their knowledge and experience.

**Author Contribution** L.E., E.D. and T.M. conceived and designed the field sampling protocol and survey. T.M. provided contacts and locations for the field surveys. L.E. conducted the field sampling, surveys and lab incubations. N.A.A. provided facilities and materials to support the lab work, while also helping with the species identifications. L.E. and E.D. performed the statistical analyses. L.E. wrote the manuscript text and prepared all the figures. All authors read and approved the manuscript.

**Funding** Open access funding provided by CIRAD. This work was supported by the European Commission through the initiative Development of Smart Innovation through Research in Agriculture (DeSIRA) Initiative, in particular the MARIGO project (Maraîchage agro-écologique péri-urbain en Côte d'Ivoire) (grant number
FOOD/2020/419–988). The first author Louise Eypert has received research support from the French *Centre de coopération internationale en recherche agronomique pour le développement* (CIRAD). The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

**Data availability** The datasets generated and analysed during the current study are openly available in “CIRAD Dataverse” at [10.18167/DVN1/ARRYMN](https://doi.org/10.18167/DVN1/ARRYMN).

## Declarations

**Competing interests** The authors declare no competing interests.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/).

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