Ecological analysis of mosquito larval communities in Burkina Faso to inform environmental monitoring of genetic control programs

Ecological analysis of mosquito larval communities in Burkina Faso to inform environmental monitoring of genetic control programs | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Ecological analysis of mosquito larval communities in Burkina Faso to inform environmental monitoring of genetic control programs Inoussa TOÉ, Mahamadi KIENTEGA, Armel Judicael LINGANI, Jérémy BOUYER, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8165654/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract In Burkina Faso, Target Malaria project developing gene drive technology targeting Anopheles coluzzii raises important ecological questions about potential non-target effects. Understanding interactions in mosquitoes' natural environments is crucial for developing effective post-release environmental safety monitoring. This study assesses the ecological exposure and potential risks to non-target organisms associated with An. coluzzii suppression. Using co-occurrence, niche overlap metrics, and characterisation of physicochemical parameters, we evaluated interspecific relationships among mosquitoes and macroinvertebrate taxa from larval habitats in Burkina Faso. Combined index revealed distinct ecological relationships, ranging from competitive or facilitative coexistence to spatial segregation driven by predation or behavioural avoidance. Based on these interactions, an exposure score was developed to quantify the potential susceptibility of non-target organisms to ecological changes following the removal of An. coluzzii . The results showed variable exposure among taxa, with An. gambiae s.s. having the highest score, followed by An. arabiensis and Culex spp. Predatory taxa such as Corixidae showed niche overlap but limited spatial co-occurrence, suggesting effective predation. The detection of hybrid forms ( An. coluzzii x An. gambiae s.s. ) further highlights the potential for gene flow. This study introduces a quantitative framework that combines ecological indices and exposure scores to predict potential risks to non-target organisms. Biological sciences/Ecology Earth and environmental sciences/Ecology Biological sciences/Zoology Anopheles coluzzii Gene drive Non-target organism Ecological exposure score environmental monitoring Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Mosquito bites are responsible for most vector-borne diseases. Africa is one of the most severely affected regions, with high burdens and mortality rates over the years 1 , 2 . Mosquitoes are widely recognised as important vectors of human disease, transmitting pathogens that cause diseases, such as malaria, dengue fever, and lymphatic filariasis 3 . In sub-Saharan Africa, Anopheles gambiae s.l. plays a key role in spreading malaria, which remains a significant public health challenge 4 . Nevertheless, Anopheles species can breed in the same locations as other mosquito genera, such as Culex and Aedes . These species also serve as important vectors for diseases, including arboviruses and lymphatic filariasis. In natural breeding habitats, competition among mosquito larvae is affected by resource availability (water, food, and shelter), water quality, and species-specific ecological adaptations. Understanding the ecological characteristics of mosquito larval habitats, such as electrical conductivity, dissolved oxygen levels, pH, salinity, CO 2 , total dissolved solids, turbidity, and temperature, is essential for developing effective vector control strategies 5 , 6 . These dynamics significantly influence larval growth, survival rates, and population patterns, ultimately affecting the emergence of adult mosquitoes, species composition, fitness, and the broader transmission of vector-borne diseases 7 , 8 . Co-occurrence refers to two or more species living or appearing together in the same place or habitat, at the same time. Deviations from the independent occurrence of two species may indicate an ecological relationship such as predation, symbiosis, or competition. Studying co-occurrence patterns can thus provide valuable insights into the ecological connections among mosquito species and with their predators, especially when their responses to environmental factors are studied in parallel, helping to identify key factors influencing species distribution 9 . The co-occurrence approach has been used to investigate potential species interactions within an ecological system. A notable co-occurrence can indicate positive interactions (mutualism and facilitation), while competition or predation are regarded as negative interactions 10 . Distribution patterns observed within a single ecosystem can be understood as species' responses to environmental conditions or to dispersal limitations. The co-occurrence foundation suggests that when species within a community interact in ways that impact each other's abundance or presence across various spaces, thereby shaping local community assembly patterns, their co-occurrence will not be random 11 . This phenomenon can be identified through appropriate sampling designs and statistical analyses 11 . For example, it may be observed that predators are found alongside their prey more frequently than expected. At the same time, competitors are generally seen together less often than would be expected from a random assembly 12 . The results of the study by Freilich et al. , highlight that a supplementary approach might complement the co-occurrence analysis, as some factors could influence the observed interactions identified as environmental variables, such as abiotic parameters, including pH, temperature, conductivity, turbidity, salinity, and % oxygen 12 . Recent advances in vector control methods, particularly gene drive technology, provide a promising strategy for reducing disease transmission by suppressing or replacing mosquito populations 13 – 15 . However, implementing gene drive strategies within the target species could disrupt the existing ecological balance by altering competitive or predatory interactions with other mosquitoes and macroinvertebrates 16 – 18 . This raises a question: “Could the targeted suppression of An. coluzzii cause an ecological imbalance, promote the emergence of new vectors, or disrupt local aquatic ecosystems?”. Indeed, such changes might enable other mosquito species to emerge and occupy the ecological niches left vacant by the decline in the target species, a phenomenon known as competitive release 19 – 21 . Such unforeseen shifts in mosquito dynamics could influence disease transmission patterns and jeopardise broader ecosystem stability, including predators that may be harmed by their suppression. Several genetic control initiatives are progressing towards field evaluations. The PRONTI (Priority Ranking of Non-target Invertebrates) system has been proposed as a framework for monitoring these interventions. Initially developed to prioritise non-target invertebrates for biosafety testing of transgenic crops 22 and later adapted for classical biological control programmes 23 . PRONTI offers a structured approach for ranking organisms based on their risk, ecological or economic importance, and suitability for monitoring. It helps users follow a systematic process to assess and balance these factors within the framework of proposed post-release monitoring programmes. This study investigates species competition, niche overlap, and co-occurrence patterns within the An. gambiae s.l . species and among other mosquitoes and macroinvertebrates in natural larval habitats. Additionally, we examined some physicochemical parameters of the larval habitats and their influence on species distribution and larval competition. We used the results to better characterise two PRONTI components for the collected species, the hazard, that is, the degree to which the suppression of An. coluzzi poses a threat to non-target organisms, and the exposure, meaning how much non-target organisms may be exposed to the stressor. This research contributed to a deeper understanding of mosquito ecology and provided essential information for post-release environmental safety monitoring of genetic tools for mosquito vector control in Burkina Faso. Results Ecology of the larval habitats Several types of ecological niches were sampled during the survey for larval collection. These sites included seven types of mosquito larval habitats (N = 138), especially the artificial habitats (N = 13), ponds (N = 14), puddles (N = 70), rice paddy fields (N = 3), river pockets (N = 9), streams (N = 11), and tyre tracks (N = 18). Most of these sites were human-made, except for the streams and river pockets. Analyses of the ecological properties of these breeding sites revealed differential ecological patterns that may influence the abundance of the different macroinvertebrate communities. Table 1 presents the average values of each measured parameter at various breeding sites, including temperature, turbidity, pH, conductivity, and oxygen level. The average temperature ranged from 26.04 ± 2.56°C to 33.82 ± 3.01°C across the different types of breeding sites. The highest temperature was observed in the river/pocket, while the lowest was in ponds. Turbidity levels varied, with an average maximum of 362.95 ± 312.18 NTU, observed in the streams. Mean pH, conductivity, and % oxygen ranged from 7.46 ± 0.50 to 8.23 ± 0.53, 61.77 ± 14.97 to 271.17 ± 246.93, and 10.37 ± 5.63 to 16.96 ± 13.94, respectively. The highest pH and conductivity of 8.23 ± 0.53 and 271.17 ± 246.93, respectively, were observed in the river pockets. The temperature was shown to influence mosquito dynamics, with significant correlations observed between temperature and the density of Anopheles spp. (r = 0.212, p = 0.012) and Culex spp. (r = 0.187, p = 0.028). Anopheles coluzzii exhibited a significant negative correlation with pH (r = -0.223, p = 0.009), while a strong positive correlation was observed with turbidity (r = 0.344, p < 0.001) and temperature (r = 0.213, p = 0.012). In contrast, for An. arabiensis , a significant correlation with conductivity was detected (r = 0.267, p = 0.002). The complete Spearman correlation data, including coefficients and p-values, are provided in Supplementary file S1. Table 1 Average and standard deviation of physicochemical parameters across habitat types from July to October 2024 pH Temperature (°C) Conductivity (µS/cm) Turbidity (NTU) Oxygen (%) Larval habitats N = 138 Artificial habitats N = 13 1 7.75 ± 0.53 28.55 ± 3.74 83.30 ± 87.38 197.73 ± 243.51 16.96 ± 13.94 Ponds N = 14 1 8.20 ± 1.31 26.04 ± 2.56 64.52 ± 64.24 305.20 ± 378.78 21.11 ± 38.96 Puddles N = 70 1 7.70 ± 0.59 31.13 ± 4.25 98.81 ± 231.83 285.72 ± 294.04 16.69 ± 14.89 Rice paddy field N = 3 1 7.58 ± 0.38 31.07 ± 1.46 61.77 ± 14.97 184.40 ± 153.18 13.93 ± 2.90 Streams N = 11 1 7.58 ± 0.63 31.36 ± 4.50 90.60 ± 208.30 362.95 ± 312.18 12.34 ± 5.73 Tyre tracks N = 18 1 7.46 ± 0.50 29.88 ± 4.25 63.81 ± 90.93 274.98 ± 238.17 10.37 ± 5.63 River/pocket N = 9 1 8.23 ± 0.53 33.82 ± 3.01 271.17 ± 246.93 32.20 ± 15.58 16.61 ± 3.52 p-value 2 0.082 < 0.001 0.053 0.017 0.068 1 Mean±Standard deviation 2 Kruskal-Wallis rank sum test N = Number of le larval habitats Species diversity and distribution A total of 7,748 larvae from three mosquito genera were collected in Pala, Bama, and Soumousso. Figure 1 depicts the distribution of mosquito genera and species from these locations. Significant differences were observed between sites, indicating real variation in the composition of genera and species in the sampling areas. Anopheles spp. was the most common mosquito genus (Kruskal-Wallis chi-squared = 188.79, df = 2, p-value < 2.2e-16), representing 70.41% (5,455/7,748) of the recovered larvae, followed by Culex spp and Aedes spp mosquitoes, which made up 21.77% (1,687/7,748) and 7.82% (606/7,748), respectively. Anopheles mosquitoes were predominant at all sites, accounting for 50% (1671/3368) in Bama, 84% (1481/1768) in Pala, and 88.2% (2303/2612) in Soumousso. A subsample of Anopheles larvae (n = 1,306 larvae) was subjected to molecular analyses for the identification of An. gambiae complex species. Among these, An. coluzzii was the most prevalent species (Kruskal-Wallis chi-squared = 73.406, df = 3, p-value = 7.96e-16), comprising 41.03% (537/1,309), while An. gambiae s.s. and An. arabiensis , accounted for 29.79% (390/1,309) and 26.81% (351/1,309), respectively. Anopheles gambiae s.s. and An. arabiensis were predominant in Soumousso, with frequencies of 47% (287/607) and 33% (203/607), respectively. Conversely, in Bama, however, An. coluzzii was predominant, constituting 84.71% (421/497), and in Pala, An. arabiensis was the most common at 58.5% (120/205), followed by An. gambiae s.s. at 34.6% (71/205). A small proportion of hybrids between An. coluzzii and An. gambiae s.s . was detected, accounting for 2.37% (31/1,309) of the mosquitoes analysed. Anopheles spp. colonises puddles, ponds, tyre tracks, river/pocket, streams, and rice paddy fields rather than artificial habitats. Additionally, macroinvertebrates, such as Baetidae, Corixidae, Nepidae, Hydrophilidae, Dytiscidae and Libellulidae were present in the same larval habitats as Anopheles spp., even if they were present in low abundance (Fig. 2 a). The proportional distribution of An. gambiae species within shared larval habitats (Fig. 2 b) provides a better understanding of community dynamics. An. coluzzii , An. arabiensis , An. gambiae s.s ., and hybrid ( coluzzii x gambiae s.s .) were found cohabiting at different proportions across almost all larval habitats. Ecological niche overlaps and species co-occurrence The Pianka and Jaccard indices were calculated to assess co-occurrence patterns between An. coluzzii , other mosquito species, competitors, and predators sharing the same habitats (Fig. 3 ). Overall, most species pairs exhibited low to moderate Pianka values, while Jaccard values ranged from very low to high. Anopheles gambiae s.s. and An. arabiensis displayed low Pianka values alongside high and moderate Jaccard values, respectively, indicating niche differentiation and co-occurrence with An. coluzzii . Similarly, Culex spp. also displayed low Pianka values combined with moderate Jaccard values, suggesting niche differentiation and partial co-occurrence. In contrast, species with low Pianka and Jaccard values, such as Aedes spp. and Baetidae (predators of Anopheles larvae), occupy distinct niches and exhibit minimal co-occurrence with An. coluzzii partially partitioned habitat. In addition to the pairwise analysis with An. coluzzii , a complementary assessment was performed using the genus Anopheles , i.e., before the identification of species by PCR, to provide a more extensive overview of co-occurrence and niche overlap patterns at the genus or family level. The results (Fig. 3 a) reveal a high value of niche overlap but limited spatial co-occurrence between Anopheles spp. and Corixidae (predator species). Exposure assessment as a basis for hazard evaluation in the suppression of Anopheles coluzzii An exposure score was calculated for each non-target species by combining Pianka and Jaccard indices with observed co-occurrence data. The score ranges from 0 (no exposure) to 1 (maximum exposure), providing a quantitative measure of potential contact or overlap with An. coluzzii (Table 2 ). Exposure scores differed among non-target organisms, with three species showing moderate exposure levels, including An. gambiae s.s ., An. arabiensis , and Culex spp. Conversely, species such as Corixidae and Baetidae had low exposure scores, indicating comparatively limited spatial overlap with An. coluzzii . Table 2 Exposure scores of the most exposed non-target organisms Non-Target Organisms Pianka index Jaccard Index Observed co-occurrence Exposure score Exposure level Anopheles gambiae s.s. 0.237 0.546 0.679 0.463 Moderate Anopheles arabiensis 0.158 0.409 0.487 0.332 Moderate Culex spp 0.158 0.337 0.436 0.295 Moderate Corixidae 0.213 0.218 0.218 0.216 Low Baetidae 0.272 0.146 0.154 0.199 Low Hybrid (coluzzii x gambiaes.s.) 0.225 0.159 0.167 0.188 Low Anopheles ND 0.110 0.182 0.205 0.160 Low Aedes spp 0.147 0.056 0.064 0.095 Low Discussion This study aimed to evaluate ecological exposure and potential hazard to non-target organisms associated with the suppression of An. coluzzii , through the combined analysis of niche overlap and co-occurrence patterns. Additionally, correlations between physicochemical parameters and taxa density were examined to understand how environmental factors may influence these interactions. This study aimed to evaluate ecological exposure and potential hazard to non-target organisms related to the suppression of An. coluzzii, through the combined analysis of niche overlap and co-occurrence patterns. Additionally, correlations between physicochemical parameters and taxa density were examined to understand how environmental factors may influence these interactions. Overall, the indices reveal a variety of co-occurrence patterns between An. coluzzii and non-target organisms, ranging from frequent coexistence despite niche differentiation to segregation across habitats. Exposure scores varied among them, with NTOs exhibiting only moderate and low exposure levels. These findings provide valuable insights into the ecological dynamics of mosquito populations, enhancing understanding of larval competition and identifying non-target organisms most likely to be affected by the suppression of An. coluzzii , which could serve as indicators for post-release monitoring. Overall, the indices reveal a variety of co-occurrence patterns between An. coluzzii and non-target organisms, ranging from frequent coexistence despite niche differentiation to segregation across habitats. Exposure scores varied among them, with NTOs showing only moderate and low levels of exposure. These findings offer valuable insights into the ecological dynamics of mosquito populations, enhancing understanding of larval competition and identifying non-target organisms most likely to be affected by the suppression of An. coluzzii , which could serve as indicators for post-release monitoring. The combined analysis of Pianka and Jaccard indices revealed varying patterns of niche overlap and spatial co-occurrence among An. coluzzii and sympatric taxa. Low Pianka values coupled with moderate Jaccard values, as observed between An. coluzzii and its two sibling species, but higher with An. gambiae s.s. than with An. arabiensis . These results indicate niche differentiation, associated with stable coexistence without intense competition, supporting an earlier study in Burkina Faso that showed a non-random distribution among these three sibling species 24 , 25 . Anopheles coluzzii was mainly associated with permanent and extensive larval habitats, such as rice fields. Furthermore, An. gambiae s.s. and An. arabiensis were mainly linked to temporary larval habitats created by rainfall. Therefore, studies have shown that An. arabiensis and An. coluzzii sometimes occupy similar larval habitats 26 . In this case, An. arabiensis significantly outcompetes An. coluzzii in mixed larval species, suggesting that even closely related mosquito species may often be more ecologically distinct than previously thought 26 , 27 . Conversely, low Pianka and moderate Jaccard values between An. coluzzii and Culex spp indicate neutral coexistence, and very limited or negligible direct competition. Onen et al., (2021) 28 , in their study in Uganda, reported partial co-existence between Culex spp. and An. gambiae s.l. , which each occupy distinct niches with no clear evidence of competition. Furthermore, Djimde et al. (2022), found that An. coluzzii cannot survive in breeding sites favourable to Culex spp 29 . Direct interactions between Corixidae and An. coluzzii were not detectable in our dataset. However, considering the wider Anopheles gambiae s.l. , the combination of moderately high Pianka and low Jaccard values indicates shared habitat use with limited co-occurrence, suggesting spatial segregation likely driven by predation or behavioural avoidance. In fact, a study has shown that Anopheles larvae tend to reduce their activity and position themselves near walls or shallow edges, areas less accessible to predators such as Notonectidae, Dytiscidae, and Libellulidae, reinforcing the avoidance hypothesis 30 . Anopheles gambiae s.s. prefers temporary habitats that are generally low in predators, while An. coluzzii exploits refuges such as vegetation or floating debris in permanent habitats 25 . These adaptations limit direct interactions, which may explain the observed segregation. In addition to behavioural avoidance, effective predation may have reduced our exposure score, since larvae are either consumed or egg-laying females may avoid microhabitats occupied by predators. This pattern is consistent with predation-driven spatial segregation and highlights the potential role of aquatic predators in shaping mosquito larval distributions 28 , 31 . Therefore, if a predator needed to be monitored after suppression of An. gambiae s.l. through gene drive, Corixidae would be the most suitable candidate based on our results. Following the niche overlap and co-occurrence index analyses, we examined the physicochemical characteristics of larval habitats to better understand the environmental factors influencing species distribution. This investigation provided valuable, insightful explanations for these patterns. Indeed, these factors can serve as reliable indicators of habitat preferences linked to key environmental conditions. Anopheles coluzzii exhibited a positive association with temperature and turbidity, as observed in a study in Cameroon, highlighting its ability to adapt to environments with varying pollution levels, especially in highly urbanised areas 32 . Conversely, turbidity was found to negatively affect the presence of An. gambiae s.s. 33 . Mosquito larvae are similar to fish like Gambusia affinis in their vulnerability to predation by visual predators in their aquatic habitats 34 . Therefore, high turbidity can hinder predators from detecting larvae, increasing their survival chances 35 . This may explain why An. coluzzii , unlike An. gambiae s.s. , shows complete indifference to predators' presence when choosing egg-laying sites 36 , indicating the species is well adapted to such environments. 37 observed that An. arabiensis prefers to breed in moderately turbid water, unlike An. coluzzii , but it can also develop in clear water. The results differ from other studies that linked An. arabiensis and An. gambiae s.s. with highly turbid waters 38 . Since food particles contribute to turbidity, these results can be suitable for mosquito larvae. In our study, electrical conductivity (EC) was positively associated with An. arabiensis in their larval habitats, despite the weak correlation. Studies conducted in Gambia and Nigeria showed a positive correlation between electrical conductivity and the abundance of An. gambiae and An. funestus larvae, indicating that moderate levels of EC may favour their survival while limiting adverse environmental factors 39 , 40 . However, other research offers a more nuanced view. For example, Chirebvu & Chimbari, (2015) linked An. arabiensis and An. gambiae s.s. with habitats characterised by low EC 41 . In contrast, our observation of a positive, though weak, correlation between EC and An. arabiensis may reflect variability in the sampling sites. These findings suggest that there is an optimal range of electrical conductivity that enables An. arabiensis to thrive. EC levels that are neither too low nor too high could reduce competition or predation risk while remaining tolerable for this species. In this study, we introduce a novel exposure score to estimate the potential ecological impact of An. coluzzii suppression on non-target organisms. The scores highlight differences in exposure among taxa, with An. gambiae s.s. showing the highest value, indicating a moderate level of exposure, followed by An. arabiensis , and Culex spp, while taxa such as Corixidae , Baetidae , and Hybrids ( An. coluzzii x An. gambiae s.s. ) exhibit low exposure levels. These patterns align with previously observed co-occurrence and niche overlap metrics, suggesting that species sharing similar habitats or resources with An. coluzzii are more likely to experience indirect effects. The high exposure score for An. gambiae s.s. could be linked to its close evolutionary and ecological relationship with An. coluzzii , from which it diverged relatively recently 42 , 43 . Although the hybrid form ( An. coluzzii x An. gambiae s.s. ) shows a low exposure score, this finding should be interpreted cautiously, especially regarding gene drive strategies. Occasional hybridisation and gene flow between the two taxa, previously documented 44 , suggests that the spread of the introduced genetic construct beyond the initially targeted species is likely. Its functionality in other species remains to be confirmed, as the gene drive project will probably target the entire An. gambiae complex rather than only An. coluzzii . Rather than predicting specific ecological outcomes, the proposed exposure score aims to identify and prioritise non-target species for ecological monitoring post-release. By incorporating co-occurrence and niche overlap, this methodology offers a practical framework for guiding monitoring efforts and identifying early ecological indicators following the suppression of An. coluzzii . Although the scoring system remains provisional and requires further validation across diverse ecological settings, it provides a useful, quantitative basis for assessing the potential exposure of non-target species and guiding adaptive monitoring strategies. It is acknowledged that it is probably overestimated for competitors compared to predators. Comparable ecological shifts have been observed following the suppression of dominant vector species in other systems. Decades ago, insecticide control led to the complete disappearance of An. funestus in southern Kenya and northern Tanzania, leading to an increase in An. rivulorum and An. parensis due to a reduction in larval competition 16 – 18 . Similarly, in western Kenya, there was a significant decline in An. gambiae s.s. , which permitted a competitive release of An. arabiensis , allowing it to dominate 45 . These cases emphasise the risk that suppression of dominant species can create vacant ecological niches and enable colonisation by secondary vectors. These historical cases reported by other studies, along with our observations, also underline the importance of continuous ecological monitoring. This investigative approach provides a valuable initial framework for assessing non-target exposure using ecological indices. Future research could expand this analysis by incorporating larger datasets and additional modelling techniques, such as null models for co-occurrence patterns 46 or ecological niche factor analysis (ENFA), to improve understanding of interaction dynamics and environmental influences. Moreover, methods like the ANOSPP amplicon panel or the use of eDNA combined with metabarcoding offer promising prospects for detailing the full aquatic community 47 – 49 . Besides identifying macroinvertebrate species, these approaches may also uncover other biological interactions, such as microbial communities or predator-prey relationships, which could influence mosquito ecology 50 . Conclusion Overall, this study provides an integrative and operational framework for assessing ecological exposure and potential hazard to non-target organisms following the suppression of An. coluzzii . By combining niche overlap and co-occurrence metrics, it identified organisms with varying levels of ecological connection, indicating interactions such as competition, spatial segregation, or predation. Various species, including An. gambiae s.s. , An. arabiensis , and Culex spp show moderate exposure scores, emphasising their potential as indicators for post-release environmental surveillance. The existence of hybrid forms ( An. coluzzii x An. gambiae s.s. ), although associated with low exposure scores, suggests possible gene flow, which, in a scenario aiming to target the gambiae complex, could facilitate the spread of suppression constructs across closely related species. Predatory organisms like Corixidae showed stronger associations at the level of the An. gambiae s.l. complex rather than individual subspecies, but it would still be interesting to consider them as ecological indicators, if such a complex were targeted, particularly since our exposure score tends to be lower for predators than competitors. This framework offers a valuable basis for predicting ecological interactions, guiding monitoring efforts, and informing risk assessments in vector control strategies. Methods Sampling sites Sampling was conducted at three sites: Bama/VK7 (11.406926°N, − 4.41175167°E), Soumousso (11.014304°N, − 4.048567°E), and Pala (11.150416°N, − 4.234323°E), in the western part of Burkina Faso (Fig. 4 ). These locations were chosen to represent the environmental, climatic, and socio-ecological diversity of the humid savannah surrounding Bobo-Dioulasso city. This region is especially significant for studying vector ecology and the patterns of vector-borne disease transmission. VK7 is within the municipality of Bama and has an estimated population of 31,215 inhabitants. It is part of an extensive irrigation scheme for rice cultivation, where perennial water flows year-round, supporting large aquatic systems conducive to mosquito breeding. Soumousso is a rural commune with approximately 7,669 residents, situated in a transitional agroecological zone featuring rainforests and semi-savannahs with open forests, which support rich biodiversity. Pala, with about 2,180 inhabitants, is a peri-urban area in Arrondissement 5 of Bobo-Dioulasso. This zone is prone to rapid, largely unplanned urban expansion characterised by a diverse interface between fragmented natural systems and growing human settlements. All three sites are within the Sudanese climatic zone, characterised by a tropical savannah climate with two distinct seasons: a rainy season from May to October, with an average annual rainfall exceeding 1,000 mm, and a dry season from November to April 51 . The dominant vegetation includes trees and open savannah forests, with gallery forests and wetlands along streams such as the Kou River, creating a mosaic of varied aquatic and semi-aquatic habitats. According to 51 , agricultural activities- mainly cotton and similar crop cultivation- along with irrigation-based rice production in Bama and rain-fed agriculture in Soumousso, greatly affect local land-use patterns and the availability of mosquito breeding sites. Characterisation of mosquito breeding habitats Field surveys were carried out in Bama (Vk7), Soumousso, and Pala from July to October 2024 to identify and gather data on 138 mosquito larval habitats. To ensure spatial representativeness within each village, their areas were divided into four sections using a cross-shaped grid, with the main pathway acting as a central axis. In each section, breeding sites were systematically searched. This approach ensured a geographically representative sampling of breeding sites, reflecting their distribution across the different quadrants of each village. It revealed that they were primarily located along pathways and streams 52 . Mosquito larvae and other macroinvertebrates were collected from their natural breeding sites (Fig. 5 ), primarily using the dipping method. It revealed that they were mainly found along pathways and streams. Mosquito larvae and other macroinvertebrates were gathered from their natural breeding sites (Fig. 5 ), mainly using the dipping method. This sampling was designed considering the size of the sampling sites as categorised below: Small habitats (< 1m in length): 2 squints (arm fully extended, bring sieve to the edge, lift content, and spread on the plate). Medium habitats (1-5m): 4 squints from 2 positions on the edge of the larval habitat. Large habitats (> 5m): 6 squints from 3 positions on the edge of the larval habitat. The collected larvae and macroinvertebrates were brought to the laboratory for species identification. Before this, the small and large stages were separated, along with the macroinvertebrates, to prevent predation. Additionally, the small Anopheles s.l. larval stages were kept until L3 and L4 for identification. In addition, the small Anopheles s.l. larval stages were kept until L3 and L4 for identification. The mosquito larvae were identified using the morphological identification keys described by Robert et al . (2022) 53 . The macroinvertebrates were similarly identified using the morphological keys detailed by Gerber and Gabriel 52 . After identification, both the mosquito larvae and macroinvertebrates were stored in 80% alcohol for subsequent analyses. Molecular analyses were performed to identify the species within the An. gambiae complex, using the SINE 200X protocol 54 . During the mosquito larval survey, geographical coordinates, physicochemical parameters, and characteristics of each breeding site were also recorded. The physicochemical parameters, such as oxygen levels, conductivity, temperature, pH, and turbidity, were measured directly in the field before larval collection using a multiparameter handheld instrument (Lovibond SensoDirect 150). Subsequently, a water sample was taken from each deposit to measure turbidity (NTU) using the Turb® 430 IR turbidimeter. Statistical analysis All statistical analyses were performed using RStudio version 4.3.2 (RStudio, Inc., Boston, MA, USA, 2016). Averages and standard deviations of water physicochemical parameters (pH, turbidity, conductivity, % oxygen, and temperature) were calculated to highlight variations across larval habitats and observation periods. To compare means among multiple groups and evaluate their associations with larval habitat, the Kruskal-Wallis test was employed. The resulting p-values were computed and incorporated into summary tables using the summary package, enabling the assessment of the statistical significance of these differences. The non-parametric Spearman correlation was used to examine the relationship between mosquito species and physicochemical parameters, with data in supplementary file S2 and R code in supplementary file S3. Larval proportions for each genus and species were calculated as a percentage of the total larvae collected or used for molecular identification, and variations were analysed using Kruskal-Wallis tests. To assess the distribution of mosquito genera by site, a Fisher's exact test with p-value simulation (10,000 replicates) was performed to explore the relationship between sampling sites and mosquito genera and species. Approach for Non-Target Organisms risk assessment Ecological risk assessment was carried out using an approach based on three complementary ecological metrics: exposure score that considers niche overlaps, spatial habitat similarity, and observed co-occurrence, each representing a dimension of habitat sharing and potential risk of interaction. This strategy has the advantage of assessing potential effects on NTOs after specific control of An coluzzii . Data (supplementary file S4) and R code (supplementary file S5) used to compute niche overlap, habitat similarity, co-occurrence rates, and the final exposure score for each non-target organism are available in the supplementary files. Pianka index (Ojk), or Pianka's niche overlap, is a symmetrical measure that indicates the level of similarity in resource category use between two species 55 . It is calculated by: \(\:{O}_{jk}=\frac{\varSigma\:\left({P}_{ij}\cdot\:{p}_{ik}\right)}{\sqrt{\varSigma\:{p}_{ij}^{2}\cdot\:\varSigma\:{p}_{ik}^{2}}}\) where: O jk : Niche overlap between species j and k . P ij and P ik : Proportions of resource i used by species j and species k , respectively. Jaccard's index (J) was utilised to measure the similarity of species observed across different habitats to identify species co-occurrence 56 . It was calculated as follows: $$\:J=\frac{a}{a+b+c}$$ a: number of sites where both species are found. b: number of sites where the first species occurs and the second does not. c: number of sites where the second species is present and the first is absent. Observed co-occurrence (OC = shared sites/target species sites) focuses on estimating the sites where the target species occurs, unlike the Jaccard index, which assesses the overall similarity. It answers when the target is present, and how often is the non-target organism also present. This measure is important to distinguish between general habitat sharing and the likelihood of direct interaction within the target's specific habitat. Integrating metrics into a composite exposure score Considering their relative ecological importance, weighting coefficients were assigned to compare each indicator against ecological exposure potential (Table 3 ). Niche overlap received the highest weight (40%), as it signifies a fundamental potential for ecological interaction through shared resource use and functional similarity. Habitat similarity, which accounts for spatial overlap as well as environmental opportunity for exposure 57 , 58 , and observed co-occurrence, reflecting actual interactions, albeit subject to environmental heterogeneity and sampling bias 59 , were both weighted 30%. Table 3 Relative weighting of ecological indicators contributing to the composite exposure score Metric Standard Description Question it answers Niche overlap ( Ojk) 40% Similarity in resource use between species Do they use the environment in the same way? Habitat similarity (J) 30% Proportion of habitats shared between two species Do they live in the same types of places? Observed co-occurrence (OC) 30% Empirical frequency of species detected together within the same sampling units, reflecting realised spatial and temporal overlap How often are two species found together? Exposition score = (Ojk × 0.4) + (J × 0.3) + (OC × 0.3) To aid in interpreting exposure scores, values were classified into four levels according to their relative magnitude (Table 4 ). This classification does not suggest direct ecological effects but offers a standardised scale for comparing potential exposure across taxa. Table 4 Classification scale for exposure score interpretation. Score range Exposure level Ecological interpretation Potential impact 0.75–1.00 Very high Strong ecological and spatial similarity Likely major impact 0.50–0.75 High Significant niche overlap Moderate to high impact 0.25–0.50 Moderate Partial interaction or occasional overlap Limited impact 0.00–0.25 Low Ecological segregation Negligible impact Declarations Ethics statement All experimental procedures related to this study were carried out following institutional and national ethical guidelines for health sciences research in Burkina Faso. The study protocol received review and approval from the Institutional Ethics Committee of the Institut de Recherche en Sciences de la Santé, under approval number 32-2022/CEIRES, on May 27, 2022. Data and code availability statement Data are provided within the manuscript or supplementary information files S2 and S4. R Scripts or codes for correlation analyses and exposure score calculation are provided in supplementary files S3 and S5. Acknowledgments We would like to thank Daniel Fabrice SANON for his support during the mosquito’s DNA extraction. Special thanks to Seydou DRABO and Djamal Youssouf TOE for advising me while analysing the data. Many thanks to Séni ILBOUDO, Guel ZILA, Noufou DIABATE, Ibrahim DIABATE, Abdou Rasmané KABRE, and Ali OUARI for assisting us with our field and laboratory activities. We are grateful to Ousmane OUEDRAOGO, who assisted with the measurement of the larval physicochemical parameters. Funding This study is supported by a Wellcome Trust grant (UNS1285360). Authors' contributions Design and implementation of the study: I.T, H.M, and A.D. Laboratory activities: I.T and A.J.L. Analysis and interpretation of data: I.T, K.M, A.K, J.B and H.M; Critical writing and revision of the article to extract its important intellectual content: I.T, K.M, A.J.L, J.B, N.T, A.K, A.M,J.K, A.D, and H.M,. Final approval of the version to be submitted: I.T, K.M, A.J.L, J.B, N.T, A.K, A.M, J.K, A.D, and H.M. 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10:04:30","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":217069,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8165654/v1/ba1b56dfa757eb2d1350f712.png"},{"id":97138661,"identity":"fd167132-db57-453d-85cc-d67c0713a755","added_by":"auto","created_at":"2025-12-01 09:59:10","extension":"png","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":264028,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8165654/v1/48b7c7ef7d4faf961088acc5.png"},{"id":97016779,"identity":"c0d441d5-f244-4582-ba64-25b22748a35c","added_by":"auto","created_at":"2025-11-28 17:17:43","extension":"png","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":412642,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8165654/v1/da50fe492dc256bf2915cee2.png"},{"id":97016771,"identity":"7a5b8aea-059c-4028-8267-a9fd4c13d7d2","added_by":"auto","created_at":"2025-11-28 17:17:43","extension":"png","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1208925,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8165654/v1/8276297d556baf358be117ae.png"},{"id":97016770,"identity":"fb8d8a58-903d-426b-8d3c-d0aa846443a9","added_by":"auto","created_at":"2025-11-28 17:17:43","extension":"xml","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":152780,"visible":true,"origin":"","legend":"","description":"","filename":"82d5d61f4b32488691e50aff669c6bcf1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8165654/v1/5c47b2b1e333f24b9de18c60.xml"},{"id":97137856,"identity":"6cb17090-c716-4dbb-a1e6-9c7ca42ebcde","added_by":"auto","created_at":"2025-12-01 09:58:14","extension":"html","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":172106,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8165654/v1/ff5c3aa0e2e7725a44c07baf.html"},{"id":97138798,"identity":"28ae74df-380b-4509-9c89-b5bbf477960c","added_by":"auto","created_at":"2025-12-01 09:59:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3096357,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of mosquitoes by sampling site according to two taxonomic levels: mosquito genera and \u003cem\u003eAnopheles gambiae \u003c/em\u003ecomplex species. Nested donuts show a circle representing different sites, each from inside to outside (Pala → Bama → Soumousso).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8165654/v1/a5e49424d8fd0a7cf34a36f6.png"},{"id":97016753,"identity":"0c696ba1-2893-44db-a53f-2baef780e3d1","added_by":"auto","created_at":"2025-11-28 17:17:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5827499,"visible":true,"origin":"","legend":"\u003cp\u003e(a)\u003cstrong\u003e \u003c/strong\u003eSankey plot of the association flows between larval habitats and species; (b) Proportions of the \u003cem\u003eAnopheles gambiae\u003c/em\u003e species by larval habitat. Figure A\u003cstrong\u003e \u003c/strong\u003eprovides the relationship between larval habitat and species, and their relative abundances and distributions\u003cstrong\u003e, \u003c/strong\u003ewhereas\u003cstrong\u003e \u003c/strong\u003eFigure 4 b\u003cstrong\u003e \u003c/strong\u003eoffers a more nuanced understanding of the community dynamics. For instance, the width of each flow indicates the strength of the association, meaning that wider flows suggest a greater abundance of a species in a particular habitat (a).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8165654/v1/ac9737ced42499a9a9647d2d.png"},{"id":97016755,"identity":"2e6a717c-832e-48fa-98bb-0d8441691ba9","added_by":"auto","created_at":"2025-11-28 17:17:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":7274349,"visible":true,"origin":"","legend":"\u003cp\u003eCombined heatmap of Pianka’s niche overlap index (upper triangle) and Jaccard’s co-occurrence index (lower triangle), (a) with \u003cem\u003eAnopheles\u003c/em\u003e sp. identified morphologically and (b) with \u003cem\u003eAnopheles\u003c/em\u003e species identified by PCR from a subsample. The colour scale illustrates the degree of niche overlap and spatial co-occurrence, with values ranging from very low (pale yellow) through intermediate levels (green to turquoise) to very high (deep blue) on a scale from 0 to 1.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8165654/v1/4ef19bd4e05da6d794313404.png"},{"id":97139592,"identity":"3b8c0e9b-169d-4bcc-acc9-f98c07969017","added_by":"auto","created_at":"2025-12-01 10:00:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":10281952,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the study area showing sample collection points.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8165654/v1/6f2318363c7b73188448451e.png"},{"id":97139468,"identity":"234ac794-37bf-406b-b18c-78ca2aa3fa82","added_by":"auto","created_at":"2025-12-01 10:00:26","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":42279229,"visible":true,"origin":"","legend":"\u003cp\u003eLarval habitats.\u003c/p\u003e\n\u003cp\u003eLocation from which samples were collected after assessing water physicochemical parameters (a: stream; b: rice paddy field; c: puddle; d: tyre; e: tyre track; f: pond)\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8165654/v1/76a879df5c7ba90984566788.png"},{"id":100614532,"identity":"9d7b05c5-b416-4449-a45b-25e7be80a435","added_by":"auto","created_at":"2026-01-19 17:21:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":65464159,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8165654/v1/24ced1aa-7401-4e32-824c-10944f4e73c8.pdf"},{"id":97140263,"identity":"7ba10ecc-5566-492e-8857-c542a628c02f","added_by":"auto","created_at":"2025-12-01 10:04:24","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":40934,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8165654/v1/08c569b75764a795d887bd7f.xlsx"},{"id":97016752,"identity":"5f6d250d-7c20-4eb0-a65b-4b5cee6b08f2","added_by":"auto","created_at":"2025-11-28 17:17:42","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20493,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8165654/v1/cbc2f6f0d3a181feb1867cfc.xlsx"},{"id":97016748,"identity":"9966a637-f2cf-493b-a7af-b63562a30c17","added_by":"auto","created_at":"2025-11-28 17:17:42","extension":"r","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":8539,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile3.r","url":"https://assets-eu.researchsquare.com/files/rs-8165654/v1/75a8179d2be06d8cbfb44a01.r"},{"id":97139631,"identity":"94b24772-5bb9-4b78-ac6c-80eaa10703c8","added_by":"auto","created_at":"2025-12-01 10:01:24","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":12329531,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8165654/v1/c17716225d8f937816cee0ed.xlsx"},{"id":97016754,"identity":"d7fb3481-8f17-40f7-9e36-c7c3fdf6dfe8","added_by":"auto","created_at":"2025-11-28 17:17:42","extension":"r","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":16379,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile5.r","url":"https://assets-eu.researchsquare.com/files/rs-8165654/v1/a86ed700d4fae5c8cacc2e76.r"}],"financialInterests":"No competing interests reported.","formattedTitle":"Ecological analysis of mosquito larval communities in Burkina Faso to inform environmental monitoring of genetic control programs","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMosquito bites are responsible for most vector-borne diseases. Africa is one of the most severely affected regions, with high burdens and mortality rates over the years \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Mosquitoes are widely recognised as important vectors of human disease, transmitting pathogens that cause diseases, such as malaria, dengue fever, and lymphatic filariasis \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. In sub-Saharan Africa, \u003cem\u003eAnopheles gambiae s.l.\u003c/em\u003e plays a key role in spreading malaria, which remains a significant public health challenge \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Nevertheless, \u003cem\u003eAnopheles\u003c/em\u003e species can breed in the same locations as other mosquito genera, such as \u003cem\u003eCulex\u003c/em\u003e and \u003cem\u003eAedes\u003c/em\u003e. These species also serve as important vectors for diseases, including arboviruses and lymphatic filariasis.\u003c/p\u003e\u003cp\u003eIn natural breeding habitats, competition among mosquito larvae is affected by resource availability (water, food, and shelter), water quality, and species-specific ecological adaptations. Understanding the ecological characteristics of mosquito larval habitats, such as electrical conductivity, dissolved oxygen levels, pH, salinity, CO\u003csub\u003e2\u003c/sub\u003e, total dissolved solids, turbidity, and temperature, is essential for developing effective vector control strategies \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. These dynamics significantly influence larval growth, survival rates, and population patterns, ultimately affecting the emergence of adult mosquitoes, species composition, fitness, and the broader transmission of vector-borne diseases \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eCo-occurrence refers to two or more species living or appearing together in the same place or habitat, at the same time. Deviations from the independent occurrence of two species may indicate an ecological relationship such as predation, symbiosis, or competition. Studying co-occurrence patterns can thus provide valuable insights into the ecological connections among mosquito species and with their predators, especially when their responses to environmental factors are studied in parallel, helping to identify key factors influencing species distribution \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. The co-occurrence approach has been used to investigate potential species interactions within an ecological system. A notable co-occurrence can indicate positive interactions (mutualism and facilitation), while competition or predation are regarded as negative interactions \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDistribution patterns observed within a single ecosystem can be understood as species' responses to environmental conditions or to dispersal limitations. The co-occurrence foundation suggests that when species within a community interact in ways that impact each other's abundance or presence across various spaces, thereby shaping local community assembly patterns, their co-occurrence will not be random \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThis phenomenon can be identified through appropriate sampling designs and statistical analyses \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. For example, it may be observed that predators are found alongside their prey more frequently than expected. At the same time, competitors are generally seen together less often than would be expected from a random assembly \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. The results of the study by Freilich \u003cem\u003eet al.\u003c/em\u003e, highlight that a supplementary approach might complement the co-occurrence analysis, as some factors could influence the observed interactions identified as environmental variables, such as abiotic parameters, including pH, temperature, conductivity, turbidity, salinity, and % oxygen \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eRecent advances in vector control methods, particularly gene drive technology, provide a promising strategy for reducing disease transmission by suppressing or replacing mosquito populations \u003csup\u003e\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. However, implementing gene drive strategies within the target species could disrupt the existing ecological balance by altering competitive or predatory interactions with other mosquitoes and macroinvertebrates \u003csup\u003e\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. This raises a question: \u0026ldquo;Could the targeted suppression of \u003cem\u003eAn. coluzzii\u003c/em\u003e cause an ecological imbalance, promote the emergence of new vectors, or disrupt local aquatic ecosystems?\u0026rdquo;. Indeed, such changes might enable other mosquito species to emerge and occupy the ecological niches left vacant by the decline in the target species, a phenomenon known as competitive release \u003csup\u003e\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Such unforeseen shifts in mosquito dynamics could influence disease transmission patterns and jeopardise broader ecosystem stability, including predators that may be harmed by their suppression. Several genetic control initiatives are progressing towards field evaluations. The PRONTI (Priority Ranking of Non-target Invertebrates) system has been proposed as a framework for monitoring these interventions. Initially developed to prioritise non-target invertebrates for biosafety testing of transgenic crops \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e and later adapted for classical biological control programmes \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. PRONTI offers a structured approach for ranking organisms based on their risk, ecological or economic importance, and suitability for monitoring. It helps users follow a systematic process to assess and balance these factors within the framework of proposed post-release monitoring programmes.\u003c/p\u003e\u003cp\u003eThis study investigates species competition, niche overlap, and co-occurrence patterns within the \u003cem\u003eAn. gambiae s.l\u003c/em\u003e. species and among other mosquitoes and macroinvertebrates in natural larval habitats. Additionally, we examined some physicochemical parameters of the larval habitats and their influence on species distribution and larval competition. We used the results to better characterise two PRONTI components for the collected species, the hazard, that is, the degree to which the suppression of \u003cem\u003eAn. coluzzi\u003c/em\u003e poses a threat to non-target organisms, and the exposure, meaning how much non-target organisms may be exposed to the stressor. This research contributed to a deeper understanding of mosquito ecology and provided essential information for post-release environmental safety monitoring of genetic tools for mosquito vector control in Burkina Faso.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eEcology of the larval habitats\u003c/h2\u003e\u003cp\u003eSeveral types of ecological niches were sampled during the survey for larval collection. These sites included seven types of mosquito larval habitats (N\u0026thinsp;=\u0026thinsp;138), especially the artificial habitats (N\u0026thinsp;=\u0026thinsp;13), ponds (N\u0026thinsp;=\u0026thinsp;14), puddles (N\u0026thinsp;=\u0026thinsp;70), rice paddy fields (N\u0026thinsp;=\u0026thinsp;3), river pockets (N\u0026thinsp;=\u0026thinsp;9), streams (N\u0026thinsp;=\u0026thinsp;11), and tyre tracks (N\u0026thinsp;=\u0026thinsp;18). Most of these sites were human-made, except for the streams and river pockets. Analyses of the ecological properties of these breeding sites revealed differential ecological patterns that may influence the abundance of the different macroinvertebrate communities. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the average values of each measured parameter at various breeding sites, including temperature, turbidity, pH, conductivity, and oxygen level. The average temperature ranged from 26.04\u0026thinsp;\u0026plusmn;\u0026thinsp;2.56\u0026deg;C to 33.82\u0026thinsp;\u0026plusmn;\u0026thinsp;3.01\u0026deg;C across the different types of breeding sites. The highest temperature was observed in the river/pocket, while the lowest was in ponds. Turbidity levels varied, with an average maximum of 362.95\u0026thinsp;\u0026plusmn;\u0026thinsp;312.18 NTU, observed in the streams. Mean pH, conductivity, and % oxygen ranged from 7.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50 to 8.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53, 61.77\u0026thinsp;\u0026plusmn;\u0026thinsp;14.97 to 271.17\u0026thinsp;\u0026plusmn;\u0026thinsp;246.93, and 10.37\u0026thinsp;\u0026plusmn;\u0026thinsp;5.63 to 16.96\u0026thinsp;\u0026plusmn;\u0026thinsp;13.94, respectively. The highest pH and conductivity of 8.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53 and 271.17\u0026thinsp;\u0026plusmn;\u0026thinsp;246.93, respectively, were observed in the river pockets. The temperature was shown to influence mosquito dynamics, with significant correlations observed between temperature and the density of \u003cem\u003eAnopheles\u003c/em\u003e spp. (r\u0026thinsp;=\u0026thinsp;0.212, p\u0026thinsp;=\u0026thinsp;0.012) and \u003cem\u003eCulex\u003c/em\u003e spp. (r\u0026thinsp;=\u0026thinsp;0.187, p\u0026thinsp;=\u0026thinsp;0.028). \u003cem\u003eAnopheles coluzzii\u003c/em\u003e exhibited a significant negative correlation with pH (r = -0.223, p\u0026thinsp;=\u0026thinsp;0.009), while a strong positive correlation was observed with turbidity (r\u0026thinsp;=\u0026thinsp;0.344, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and temperature (r\u0026thinsp;=\u0026thinsp;0.213, p\u0026thinsp;=\u0026thinsp;0.012). In contrast, for \u003cem\u003eAn. arabiensis\u003c/em\u003e, a significant correlation with conductivity was detected (r\u0026thinsp;=\u0026thinsp;0.267, p\u0026thinsp;=\u0026thinsp;0.002). The complete Spearman correlation data, including coefficients and p-values, are provided in Supplementary file S1.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAverage and standard deviation of physicochemical parameters across habitat types from July to October 2024\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003epH\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTemperature (\u0026deg;C)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eConductivity (\u0026micro;S/cm)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTurbidity (NTU)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eOxygen (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003e\u003cb\u003eLarval habitats N\u0026thinsp;=\u0026thinsp;138\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eArtificial habitats\u003c/b\u003e N\u0026thinsp;=\u0026thinsp;13\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.55\u0026thinsp;\u0026plusmn;\u0026thinsp;3.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e83.30\u0026thinsp;\u0026plusmn;\u0026thinsp;87.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e197.73\u0026thinsp;\u0026plusmn;\u0026thinsp;243.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e16.96\u0026thinsp;\u0026plusmn;\u0026thinsp;13.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ePonds\u003c/b\u003e N\u0026thinsp;=\u0026thinsp;14\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.20\u0026thinsp;\u0026plusmn;\u0026thinsp;1.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.04\u0026thinsp;\u0026plusmn;\u0026thinsp;2.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e64.52\u0026thinsp;\u0026plusmn;\u0026thinsp;64.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e305.20\u0026thinsp;\u0026plusmn;\u0026thinsp;378.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e21.11\u0026thinsp;\u0026plusmn;\u0026thinsp;38.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ePuddles\u003c/b\u003e N\u0026thinsp;=\u0026thinsp;70\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.13\u0026thinsp;\u0026plusmn;\u0026thinsp;4.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e98.81\u0026thinsp;\u0026plusmn;\u0026thinsp;231.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e285.72\u0026thinsp;\u0026plusmn;\u0026thinsp;294.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e16.69\u0026thinsp;\u0026plusmn;\u0026thinsp;14.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRice paddy field\u003c/b\u003e N\u0026thinsp;=\u0026thinsp;3\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.07\u0026thinsp;\u0026plusmn;\u0026thinsp;1.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e61.77\u0026thinsp;\u0026plusmn;\u0026thinsp;14.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e184.40\u0026thinsp;\u0026plusmn;\u0026thinsp;153.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e13.93\u0026thinsp;\u0026plusmn;\u0026thinsp;2.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eStreams\u003c/b\u003e \u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;11\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.36\u0026thinsp;\u0026plusmn;\u0026thinsp;4.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e90.60\u0026thinsp;\u0026plusmn;\u0026thinsp;208.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e362.95\u0026thinsp;\u0026plusmn;\u0026thinsp;312.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12.34\u0026thinsp;\u0026plusmn;\u0026thinsp;5.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eTyre tracks\u003c/b\u003e\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;18\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.88\u0026thinsp;\u0026plusmn;\u0026thinsp;4.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e63.81\u0026thinsp;\u0026plusmn;\u0026thinsp;90.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e274.98\u0026thinsp;\u0026plusmn;\u0026thinsp;238.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10.37\u0026thinsp;\u0026plusmn;\u0026thinsp;5.63\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRiver/pocket\u003c/b\u003e N\u0026thinsp;=\u0026thinsp;9\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.82\u0026thinsp;\u0026plusmn;\u0026thinsp;3.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e271.17\u0026thinsp;\u0026plusmn;\u0026thinsp;246.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e32.20\u0026thinsp;\u0026plusmn;\u0026thinsp;15.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e16.61\u0026thinsp;\u0026plusmn;\u0026thinsp;3.52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e \u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.082\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.053\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.068\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eMean\u0026plusmn;Standard deviation\u003c/p\u003e\u003cp\u003e\u003csup\u003e2\u003c/sup\u003e Kruskal-Wallis rank sum test\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;Number of le larval habitats\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSpecies diversity and distribution\u003c/h3\u003e\n\u003cp\u003eA total of 7,748 larvae from three mosquito genera were collected in Pala, Bama, and Soumousso. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e depicts the distribution of mosquito genera and species from these locations. Significant differences were observed between sites, indicating real variation in the composition of genera and species in the sampling areas. \u003cem\u003eAnopheles\u003c/em\u003e spp. was the most common mosquito genus (Kruskal-Wallis chi-squared\u0026thinsp;=\u0026thinsp;188.79, df\u0026thinsp;=\u0026thinsp;2, p-value\u0026thinsp;\u0026lt;\u0026thinsp;2.2e-16), representing 70.41% (5,455/7,748) of the recovered larvae, followed by \u003cem\u003eCulex\u003c/em\u003e spp and \u003cem\u003eAedes\u003c/em\u003e spp mosquitoes, which made up 21.77% (1,687/7,748) and 7.82% (606/7,748), respectively. \u003cem\u003eAnopheles\u003c/em\u003e mosquitoes were predominant at all sites, accounting for 50% (1671/3368) in Bama, 84% (1481/1768) in Pala, and 88.2% (2303/2612) in Soumousso. A subsample of \u003cem\u003eAnopheles\u003c/em\u003e larvae (n\u0026thinsp;=\u0026thinsp;1,306 larvae) was subjected to molecular analyses for the identification of \u003cem\u003eAn. gambiae\u003c/em\u003e complex species.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAmong these, \u003cem\u003eAn. coluzzii\u003c/em\u003e was the most prevalent species (Kruskal-Wallis chi-squared\u0026thinsp;=\u0026thinsp;73.406, df\u0026thinsp;=\u0026thinsp;3, p-value\u0026thinsp;=\u0026thinsp;7.96e-16), comprising 41.03% (537/1,309), while \u003cem\u003eAn. gambiae s.s.\u003c/em\u003e and \u003cem\u003eAn. arabiensis\u003c/em\u003e, accounted for 29.79% (390/1,309) and 26.81% (351/1,309), respectively. \u003cem\u003eAnopheles gambiae s.s.\u003c/em\u003e and \u003cem\u003eAn. arabiensis\u003c/em\u003e were predominant in Soumousso, with frequencies of 47% (287/607) and 33% (203/607), respectively. Conversely, in Bama, however, \u003cem\u003eAn. coluzzii\u003c/em\u003e was predominant, constituting 84.71% (421/497), and in Pala, \u003cem\u003eAn. arabiensis\u003c/em\u003e was the most common at 58.5% (120/205), followed by \u003cem\u003eAn. gambiae s.s.\u003c/em\u003e at 34.6% (71/205). A small proportion of hybrids between \u003cem\u003eAn. coluzzii\u003c/em\u003e and \u003cem\u003eAn. gambiae s.s\u003c/em\u003e. was detected, accounting for 2.37% (31/1,309) of the mosquitoes analysed.\u003c/p\u003e\u003cp\u003e\u003cem\u003eAnopheles\u003c/em\u003e spp. colonises puddles, ponds, tyre tracks, river/pocket, streams, and rice paddy fields rather than artificial habitats. Additionally, macroinvertebrates, such as Baetidae, Corixidae, Nepidae, Hydrophilidae, Dytiscidae and Libellulidae were present in the same larval habitats as \u003cem\u003eAnopheles\u003c/em\u003e spp., even if they were present in low abundance (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The proportional distribution of \u003cem\u003eAn. gambiae\u003c/em\u003e species within shared larval habitats (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb) provides a better understanding of community dynamics. \u003cem\u003eAn. coluzzii\u003c/em\u003e, \u003cem\u003eAn. arabiensis\u003c/em\u003e, \u003cem\u003eAn. gambiae s.s\u003c/em\u003e., and hybrid (\u003cem\u003ecoluzzii x gambiae s.s\u003c/em\u003e.) were found cohabiting at different proportions across almost all larval habitats.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eEcological niche overlaps and species co-occurrence\u003c/h3\u003e\n\u003cp\u003eThe Pianka and Jaccard indices were calculated to assess co-occurrence patterns between \u003cem\u003eAn. coluzzii\u003c/em\u003e, other mosquito species, competitors, and predators sharing the same habitats (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Overall, most species pairs exhibited low to moderate Pianka values, while Jaccard values ranged from very low to high. \u003cem\u003eAnopheles gambiae s.s.\u003c/em\u003e and \u003cem\u003eAn. arabiensis\u003c/em\u003e displayed low Pianka values alongside high and moderate Jaccard values, respectively, indicating niche differentiation and co-occurrence with \u003cem\u003eAn. coluzzii\u003c/em\u003e. Similarly, \u003cem\u003eCulex\u003c/em\u003e spp. also displayed low Pianka values combined with moderate Jaccard values, suggesting niche differentiation and partial co-occurrence. In contrast, species with low Pianka and Jaccard values, such as \u003cem\u003eAedes\u003c/em\u003e spp. and Baetidae (predators of \u003cem\u003eAnopheles\u003c/em\u003e larvae), occupy distinct niches and exhibit minimal co-occurrence with \u003cem\u003eAn. coluzzii\u003c/em\u003e partially partitioned habitat. In addition to the pairwise analysis with \u003cem\u003eAn. coluzzii\u003c/em\u003e, a complementary assessment was performed using the genus \u003cem\u003eAnopheles\u003c/em\u003e, i.e., before the identification of species by PCR, to provide a more extensive overview of co-occurrence and niche overlap patterns at the genus or family level. The results (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea) reveal a high value of niche overlap but limited spatial co-occurrence between \u003cem\u003eAnopheles\u003c/em\u003e spp. and Corixidae (predator species).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eExposure assessment as a basis for hazard evaluation in the suppression of Anopheles coluzzii\u003c/h3\u003e\n\u003cp\u003eAn exposure score was calculated for each non-target species by combining Pianka and Jaccard indices with observed co-occurrence data. The score ranges from 0 (no exposure) to 1 (maximum exposure), providing a quantitative measure of potential contact or overlap with \u003cem\u003eAn. coluzzii\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Exposure scores differed among non-target organisms, with three species showing moderate exposure levels, including \u003cem\u003eAn. gambiae s.s\u003c/em\u003e., \u003cem\u003eAn. arabiensis\u003c/em\u003e, and Culex spp. Conversely, species such as Corixidae and Baetidae had low exposure scores, indicating comparatively limited spatial overlap with \u003cem\u003eAn. coluzzii\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eExposure scores of the most exposed non-target organisms\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Target Organisms\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePianka index\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJaccard Index\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eObserved\u003c/p\u003e\u003cp\u003eco-occurrence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eExposure score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eExposure level\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAnopheles\u0026nbsp;gambiae\u0026nbsp;s.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.546\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.679\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.463\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAnopheles\u0026nbsp;arabiensis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.409\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.487\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.332\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCulex\u003c/em\u003e spp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.337\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.436\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.295\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCorixidae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.218\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.218\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.216\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBaetidae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.272\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.199\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHybrid\u0026nbsp;(coluzzii x gambiaes.s.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.225\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.188\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAnopheles\u003c/em\u003e\u0026nbsp;ND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAedes\u003c/em\u003e spp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to evaluate ecological exposure and potential hazard to non-target organisms associated with the suppression of \u003cem\u003eAn. coluzzii\u003c/em\u003e, through the combined analysis of niche overlap and co-occurrence patterns. Additionally, correlations between physicochemical parameters and taxa density were examined to understand how environmental factors may influence these interactions. This study aimed to evaluate ecological exposure and potential hazard to non-target organisms related to the suppression of An. coluzzii, through the combined analysis of niche overlap and co-occurrence patterns. Additionally, correlations between physicochemical parameters and taxa density were examined to understand how environmental factors may influence these interactions. Overall, the indices reveal a variety of co-occurrence patterns between \u003cem\u003eAn. coluzzii\u003c/em\u003e and non-target organisms, ranging from frequent coexistence despite niche differentiation to segregation across habitats. Exposure scores varied among them, with NTOs exhibiting only moderate and low exposure levels. These findings provide valuable insights into the ecological dynamics of mosquito populations, enhancing understanding of larval competition and identifying non-target organisms most likely to be affected by the suppression of \u003cem\u003eAn. coluzzii\u003c/em\u003e, which could serve as indicators for post-release monitoring. Overall, the indices reveal a variety of co-occurrence patterns between \u003cem\u003eAn. coluzzii\u003c/em\u003e and non-target organisms, ranging from frequent coexistence despite niche differentiation to segregation across habitats. Exposure scores varied among them, with NTOs showing only moderate and low levels of exposure. These findings offer valuable insights into the ecological dynamics of mosquito populations, enhancing understanding of larval competition and identifying non-target organisms most likely to be affected by the suppression of \u003cem\u003eAn. coluzzii\u003c/em\u003e, which could serve as indicators for post-release monitoring.\u003c/p\u003e\u003cp\u003eThe combined analysis of Pianka and Jaccard indices revealed varying patterns of niche overlap and spatial co-occurrence among \u003cem\u003eAn. coluzzii\u003c/em\u003e and sympatric taxa. Low Pianka values coupled with moderate Jaccard values, as observed between \u003cem\u003eAn. coluzzii\u003c/em\u003e and its two sibling species, but higher with \u003cem\u003eAn. gambiae s.s.\u003c/em\u003e than with \u003cem\u003eAn. arabiensis\u003c/em\u003e. These results indicate niche differentiation, associated with stable coexistence without intense competition, supporting an earlier study in Burkina Faso that showed a non-random distribution among these three sibling species \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eAnopheles coluzzii\u003c/em\u003e was mainly associated with permanent and extensive larval habitats, such as rice fields. Furthermore, \u003cem\u003eAn. gambiae s.s.\u003c/em\u003e and \u003cem\u003eAn. arabiensis\u003c/em\u003e were mainly linked to temporary larval habitats created by rainfall. Therefore, studies have shown that \u003cem\u003eAn. arabiensis\u003c/em\u003e and \u003cem\u003eAn. coluzzii\u003c/em\u003e sometimes occupy similar larval habitats \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. In this case, \u003cem\u003eAn. arabiensis\u003c/em\u003e significantly outcompetes \u003cem\u003eAn. coluzzii\u003c/em\u003e in mixed larval species, suggesting that even closely related mosquito species may often be more ecologically distinct than previously thought \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Conversely, low Pianka and moderate Jaccard values between \u003cem\u003eAn. coluzzii\u003c/em\u003e and \u003cem\u003eCulex\u003c/em\u003e spp indicate neutral coexistence, and very limited or negligible direct competition. Onen et al., (2021)\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, in their study in Uganda, reported partial co-existence between \u003cem\u003eCulex\u003c/em\u003e spp. and \u003cem\u003eAn. gambiae s.l.\u003c/em\u003e, which each occupy distinct niches with no clear evidence of competition. Furthermore, Djimde et al. (2022), found that \u003cem\u003eAn. coluzzii\u003c/em\u003e cannot survive in breeding sites favourable to \u003cem\u003eCulex\u003c/em\u003e spp\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDirect interactions between Corixidae and \u003cem\u003eAn. coluzzii\u003c/em\u003e were not detectable in our dataset. However, considering the wider \u003cem\u003eAnopheles gambiae s.l.\u003c/em\u003e, the combination of moderately high Pianka and low Jaccard values indicates shared habitat use with limited co-occurrence, suggesting spatial segregation likely driven by predation or behavioural avoidance. In fact, a study has shown that \u003cem\u003eAnopheles\u003c/em\u003e larvae tend to reduce their activity and position themselves near walls or shallow edges, areas less accessible to predators such as Notonectidae, Dytiscidae, and Libellulidae, reinforcing the avoidance hypothesis \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eAnopheles gambiae s.s.\u003c/em\u003e prefers temporary habitats that are generally low in predators, while \u003cem\u003eAn. coluzzii\u003c/em\u003e exploits refuges such as vegetation or floating debris in permanent habitats \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. These adaptations limit direct interactions, which may explain the observed segregation. In addition to behavioural avoidance, effective predation may have reduced our exposure score, since larvae are either consumed or egg-laying females may avoid microhabitats occupied by predators. This pattern is consistent with predation-driven spatial segregation and highlights the potential role of aquatic predators in shaping mosquito larval distributions \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Therefore, if a predator needed to be monitored after suppression of \u003cem\u003eAn. gambiae s.l.\u003c/em\u003e through gene drive, Corixidae would be the most suitable candidate based on our results.\u003c/p\u003e\u003cp\u003eFollowing the niche overlap and co-occurrence index analyses, we examined the physicochemical characteristics of larval habitats to better understand the environmental factors influencing species distribution. This investigation provided valuable, insightful explanations for these patterns. Indeed, these factors can serve as reliable indicators of habitat preferences linked to key environmental conditions. \u003cem\u003eAnopheles coluzzii\u003c/em\u003e exhibited a positive association with temperature and turbidity, as observed in a study in Cameroon, highlighting its ability to adapt to environments with varying pollution levels, especially in highly urbanised areas \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Conversely, turbidity was found to negatively affect the presence of \u003cem\u003eAn. gambiae s.s.\u003c/em\u003e \u003csup\u003e33\u003c/sup\u003e. Mosquito larvae are similar to fish like \u003cem\u003eGambusia affinis\u003c/em\u003e in their vulnerability to predation by visual predators in their aquatic habitats \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Therefore, high turbidity can hinder predators from detecting larvae, increasing their survival chances \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. This may explain why \u003cem\u003eAn. coluzzii\u003c/em\u003e, unlike \u003cem\u003eAn. gambiae s.s.\u003c/em\u003e, shows complete indifference to predators' presence when choosing egg-laying sites \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, indicating the species is well adapted to such environments. \u003csup\u003e37\u003c/sup\u003e observed that \u003cem\u003eAn. arabiensis\u003c/em\u003e prefers to breed in moderately turbid water, unlike \u003cem\u003eAn. coluzzii\u003c/em\u003e, but it can also develop in clear water. The results differ from other studies that linked \u003cem\u003eAn. arabiensis\u003c/em\u003e and \u003cem\u003eAn. gambiae s.s.\u003c/em\u003e with highly turbid waters \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Since food particles contribute to turbidity, these results can be suitable for mosquito larvae. In our study, electrical conductivity (EC) was positively associated with \u003cem\u003eAn. arabiensis\u003c/em\u003e in their larval habitats, despite the weak correlation. Studies conducted in Gambia and Nigeria showed a positive correlation between electrical conductivity and the abundance of \u003cem\u003eAn. gambiae\u003c/em\u003e and \u003cem\u003eAn. funestus\u003c/em\u003e larvae, indicating that moderate levels of EC may favour their survival while limiting adverse environmental factors \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. However, other research offers a more nuanced view. For example, Chirebvu \u0026amp; Chimbari, (2015) linked \u003cem\u003eAn. arabiensis\u003c/em\u003e and \u003cem\u003eAn. gambiae s.s.\u003c/em\u003e with habitats characterised by low EC\u003csup\u003e41\u003c/sup\u003e. In contrast, our observation of a positive, though weak, correlation between EC and \u003cem\u003eAn. arabiensis\u003c/em\u003e may reflect variability in the sampling sites. These findings suggest that there is an optimal range of electrical conductivity that enables \u003cem\u003eAn. arabiensis\u003c/em\u003e to thrive. EC levels that are neither too low nor too high could reduce competition or predation risk while remaining tolerable for this species.\u003c/p\u003e\u003cp\u003eIn this study, we introduce a novel exposure score to estimate the potential ecological impact of \u003cem\u003eAn. coluzzii\u003c/em\u003e suppression on non-target organisms. The scores highlight differences in exposure among taxa, with \u003cem\u003eAn. gambiae s.s.\u003c/em\u003e showing the highest value, indicating a moderate level of exposure, followed by \u003cem\u003eAn. arabiensis\u003c/em\u003e, and \u003cem\u003eCulex\u003c/em\u003e spp, while taxa such as \u003cem\u003eCorixidae\u003c/em\u003e, \u003cem\u003eBaetidae\u003c/em\u003e, and Hybrids (\u003cem\u003eAn. coluzzii x An. gambiae s.s.\u003c/em\u003e) exhibit low exposure levels. These patterns align with previously observed co-occurrence and niche overlap metrics, suggesting that species sharing similar habitats or resources with \u003cem\u003eAn. coluzzii\u003c/em\u003e are more likely to experience indirect effects. The high exposure score for \u003cem\u003eAn. gambiae s.s.\u003c/em\u003e could be linked to its close evolutionary and ecological relationship with \u003cem\u003eAn. coluzzii\u003c/em\u003e, from which it diverged relatively recently \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Although the hybrid form (\u003cem\u003eAn. coluzzii x An. gambiae s.s.\u003c/em\u003e) shows a low exposure score, this finding should be interpreted cautiously, especially regarding gene drive strategies. Occasional hybridisation and gene flow between the two taxa, previously documented \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, suggests that the spread of the introduced genetic construct beyond the initially targeted species is likely. Its functionality in other species remains to be confirmed, as the gene drive project will probably target the entire \u003cem\u003eAn. gambiae\u003c/em\u003e complex rather than only \u003cem\u003eAn. coluzzii\u003c/em\u003e. Rather than predicting specific ecological outcomes, the proposed exposure score aims to identify and prioritise non-target species for ecological monitoring post-release. By incorporating co-occurrence and niche overlap, this methodology offers a practical framework for guiding monitoring efforts and identifying early ecological indicators following the suppression of \u003cem\u003eAn. coluzzii\u003c/em\u003e. Although the scoring system remains provisional and requires further validation across diverse ecological settings, it provides a useful, quantitative basis for assessing the potential exposure of non-target species and guiding adaptive monitoring strategies. It is acknowledged that it is probably overestimated for competitors compared to predators.\u003c/p\u003e\u003cp\u003eComparable ecological shifts have been observed following the suppression of dominant vector species in other systems. Decades ago, insecticide control led to the complete disappearance of \u003cem\u003eAn. funestus\u003c/em\u003e in southern Kenya and northern Tanzania, leading to an increase in \u003cem\u003eAn. rivulorum\u003c/em\u003e and \u003cem\u003eAn. parensis\u003c/em\u003e due to a reduction in larval competition \u003csup\u003e\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Similarly, in western Kenya, there was a significant decline in \u003cem\u003eAn. gambiae s.s.\u003c/em\u003e, which permitted a competitive release of \u003cem\u003eAn. arabiensis\u003c/em\u003e, allowing it to dominate \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. These cases emphasise the risk that suppression of dominant species can create vacant ecological niches and enable colonisation by secondary vectors. These historical cases reported by other studies, along with our observations, also underline the importance of continuous ecological monitoring.\u003c/p\u003e\u003cp\u003eThis investigative approach provides a valuable initial framework for assessing non-target exposure using ecological indices. Future research could expand this analysis by incorporating larger datasets and additional modelling techniques, such as null models for co-occurrence patterns \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e or ecological niche factor analysis (ENFA), to improve understanding of interaction dynamics and environmental influences. Moreover, methods like the ANOSPP amplicon panel or the use of eDNA combined with metabarcoding offer promising prospects for detailing the full aquatic community \u003csup\u003e\u003cspan additionalcitationids=\"CR48\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Besides identifying macroinvertebrate species, these approaches may also uncover other biological interactions, such as microbial communities or predator-prey relationships, which could influence mosquito ecology \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOverall, this study provides an integrative and operational framework for assessing ecological exposure and potential hazard to non-target organisms following the suppression of \u003cem\u003eAn. coluzzii\u003c/em\u003e. By combining niche overlap and co-occurrence metrics, it identified organisms with varying levels of ecological connection, indicating interactions such as competition, spatial segregation, or predation. Various species, including \u003cem\u003eAn. gambiae s.s.\u003c/em\u003e, \u003cem\u003eAn. arabiensis\u003c/em\u003e, and \u003cem\u003eCulex\u003c/em\u003e spp show moderate exposure scores, emphasising their potential as indicators for post-release environmental surveillance. The existence of hybrid forms (\u003cem\u003eAn. coluzzii\u003c/em\u003e x \u003cem\u003eAn. gambiae s.s.\u003c/em\u003e), although associated with low exposure scores, suggests possible gene flow, which, in a scenario aiming to target the \u003cem\u003egambiae\u003c/em\u003e complex, could facilitate the spread of suppression constructs across closely related species. Predatory organisms like Corixidae showed stronger associations at the level of the \u003cem\u003eAn. gambiae s.l.\u003c/em\u003e complex rather than individual subspecies, but it would still be interesting to consider them as ecological indicators, if such a complex were targeted, particularly since our exposure score tends to be lower for predators than competitors. This framework offers a valuable basis for predicting ecological interactions, guiding monitoring efforts, and informing risk assessments in vector control strategies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eSampling sites\u003c/h2\u003e\u003cp\u003eSampling was conducted at three sites: Bama/VK7 (11.406926\u0026deg;N, \u0026minus;\u0026thinsp;4.41175167\u0026deg;E), Soumousso (11.014304\u0026deg;N, \u0026minus;\u0026thinsp;4.048567\u0026deg;E), and Pala (11.150416\u0026deg;N, \u0026minus;\u0026thinsp;4.234323\u0026deg;E), in the western part of Burkina Faso (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These locations were chosen to represent the environmental, climatic, and socio-ecological diversity of the humid savannah surrounding Bobo-Dioulasso city. This region is especially significant for studying vector ecology and the patterns of vector-borne disease transmission. VK7 is within the municipality of Bama and has an estimated population of 31,215 inhabitants. It is part of an extensive irrigation scheme for rice cultivation, where perennial water flows year-round, supporting large aquatic systems conducive to mosquito breeding. Soumousso is a rural commune with approximately 7,669 residents, situated in a transitional agroecological zone featuring rainforests and semi-savannahs with open forests, which support rich biodiversity. Pala, with about 2,180 inhabitants, is a peri-urban area in Arrondissement 5 of Bobo-Dioulasso. This zone is prone to rapid, largely unplanned urban expansion characterised by a diverse interface between fragmented natural systems and growing human settlements. All three sites are within the Sudanese climatic zone, characterised by a tropical savannah climate with two distinct seasons: a rainy season from May to October, with an average annual rainfall exceeding 1,000 mm, and a dry season from November to April \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. The dominant vegetation includes trees and open savannah forests, with gallery forests and wetlands along streams such as the Kou River, creating a mosaic of varied aquatic and semi-aquatic habitats. According to \u003csup\u003e51\u003c/sup\u003e, agricultural activities- mainly cotton and similar crop cultivation- along with irrigation-based rice production in Bama and rain-fed agriculture in Soumousso, greatly affect local land-use patterns and the availability of mosquito breeding sites.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eCharacterisation of mosquito breeding habitats\u003c/h2\u003e\u003cp\u003eField surveys were carried out in Bama (Vk7), Soumousso, and Pala from July to October 2024 to identify and gather data on 138 mosquito larval habitats. To ensure spatial representativeness within each village, their areas were divided into four sections using a cross-shaped grid, with the main pathway acting as a central axis. In each section, breeding sites were systematically searched. This approach ensured a geographically representative sampling of breeding sites, reflecting their distribution across the different quadrants of each village. It revealed that they were primarily located along pathways and streams \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Mosquito larvae and other macroinvertebrates were collected from their natural breeding sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), primarily using the dipping method. It revealed that they were mainly found along pathways and streams. Mosquito larvae and other macroinvertebrates were gathered from their natural breeding sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), mainly using the dipping method. This sampling was designed considering the size of the sampling sites as categorised below:\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eSmall habitats (\u0026lt;\u0026thinsp;1m in length): 2 squints (arm fully extended, bring sieve to the edge, lift content, and spread on the plate).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eMedium habitats (1-5m): 4 squints from 2 positions on the edge of the larval habitat.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eLarge habitats (\u0026gt;\u0026thinsp;5m): 6 squints from 3 positions on the edge of the larval habitat.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThe collected larvae and macroinvertebrates were brought to the laboratory for species identification. Before this, the small and large stages were separated, along with the macroinvertebrates, to prevent predation. Additionally, the small \u003cem\u003eAnopheles s.l.\u003c/em\u003e larval stages were kept until L3 and L4 for identification. In addition, the small \u003cem\u003eAnopheles s.l.\u003c/em\u003e larval stages were kept until L3 and L4 for identification. The mosquito larvae were identified using the morphological identification keys described by Robert \u003cem\u003eet al\u003c/em\u003e. (2022) \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. The macroinvertebrates were similarly identified using the morphological keys detailed by Gerber and Gabriel\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. After identification, both the mosquito larvae and macroinvertebrates were stored in 80% alcohol for subsequent analyses. Molecular analyses were performed to identify the species within the \u003cem\u003eAn. gambiae\u003c/em\u003e complex, using the SINE 200X protocol \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. During the mosquito larval survey, geographical coordinates, physicochemical parameters, and characteristics of each breeding site were also recorded. The physicochemical parameters, such as oxygen levels, conductivity, temperature, pH, and turbidity, were measured directly in the field before larval collection using a multiparameter handheld instrument (Lovibond SensoDirect 150). Subsequently, a water sample was taken from each deposit to measure turbidity (NTU) using the Turb\u0026reg; 430 IR turbidimeter.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed using RStudio version 4.3.2 (RStudio, Inc., Boston, MA, USA, 2016). Averages and standard deviations of water physicochemical parameters (pH, turbidity, conductivity, % oxygen, and temperature) were calculated to highlight variations across larval habitats and observation periods. To compare means among multiple groups and evaluate their associations with larval habitat, the Kruskal-Wallis test was employed. The resulting p-values were computed and incorporated into summary tables using the summary package, enabling the assessment of the statistical significance of these differences. The non-parametric Spearman correlation was used to examine the relationship between mosquito species and physicochemical parameters, with data in supplementary file S2 and R code in supplementary file S3. Larval proportions for each genus and species were calculated as a percentage of the total larvae collected or used for molecular identification, and variations were analysed using Kruskal-Wallis tests. To assess the distribution of mosquito genera by site, a Fisher's exact test with p-value simulation (10,000 replicates) was performed to explore the relationship between sampling sites and mosquito genera and species.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eApproach for Non-Target Organisms risk assessment\u003c/h2\u003e\u003cp\u003eEcological risk assessment was carried out using an approach based on three complementary ecological metrics: exposure score that considers niche overlaps, spatial habitat similarity, and observed co-occurrence, each representing a dimension of habitat sharing and potential risk of interaction. This strategy has the advantage of assessing potential effects on NTOs after specific control of \u003cem\u003eAn coluzzii\u003c/em\u003e. Data (supplementary file S4) and R code (supplementary file S5) used to compute niche overlap, habitat similarity, co-occurrence rates, and the final exposure score for each non-target organism are available in the supplementary files.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePianka index\u003c/b\u003e (Ojk), or Pianka's niche overlap, is a symmetrical measure that indicates the level of similarity in resource category use between two species \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. It is calculated by:\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{O}_{jk}=\\frac{\\varSigma\\:\\left({P}_{ij}\\cdot\\:{p}_{ik}\\right)}{\\sqrt{\\varSigma\\:{p}_{ij}^{2}\\cdot\\:\\varSigma\\:{p}_{ik}^{2}}}\\)\u003c/span\u003e\u003c/span\u003ewhere:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eO\u003c/em\u003e\u003csub\u003e\u003cem\u003ejk\u003c/em\u003e\u003c/sub\u003e: Niche overlap between species \u003cem\u003ej\u003c/em\u003e and \u003cem\u003ek\u003c/em\u003e.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eik\u003c/em\u003e\u003c/sub\u003e: Proportions of resource \u003cem\u003ei\u003c/em\u003e used by species \u003cem\u003ej\u003c/em\u003e and species \u003cem\u003ek\u003c/em\u003e, respectively.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eJaccard's index (J)\u003c/b\u003e was utilised to measure the similarity of species observed across different habitats to identify species co-occurrence \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. It was calculated as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:J=\\frac{a}{a+b+c}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003ea: number of sites where both species are found.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eb: number of sites where the first species occurs and the second does not.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ec: number of sites where the second species is present and the first is absent.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eObserved co-occurrence\u003c/b\u003e (OC\u0026thinsp;=\u0026thinsp;shared sites/target species sites) focuses on estimating the sites where the target species occurs, unlike the Jaccard index, which assesses the overall similarity. It answers when the target is present, and how often is the non-target organism also present. This measure is important to distinguish between general habitat sharing and the likelihood of direct interaction within the target's specific habitat.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eIntegrating metrics into a composite exposure score\u003c/h2\u003e\u003cp\u003eConsidering their relative ecological importance, weighting coefficients were assigned to compare each indicator against ecological exposure potential (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Niche overlap received the highest weight (40%), as it signifies a fundamental potential for ecological interaction through shared resource use and functional similarity. Habitat similarity, which accounts for spatial overlap as well as environmental opportunity for exposure \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e, and observed co-occurrence, reflecting actual interactions, albeit subject to environmental heterogeneity and sampling bias \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e, were both weighted 30%.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRelative weighting of ecological indicators contributing to the composite exposure score\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetric\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStandard\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQuestion it answers\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNiche overlap (\u003cb\u003eOjk)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSimilarity in resource use between species\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDo they use the environment in the same way?\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHabitat similarity \u003cb\u003e(J)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProportion of habitats shared between two species\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDo they live in the same types of places?\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObserved co-occurrence \u003cb\u003e(OC)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEmpirical frequency of species detected together within the same sampling units, reflecting realised spatial and temporal overlap\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHow often are two species found together?\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eExposition score = (Ojk \u0026times; 0.4) + (J \u0026times; 0.3) + (OC \u0026times; 0.3)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTo aid in interpreting exposure scores, values were classified into four levels according to their relative magnitude (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This classification does not suggest direct ecological effects but offers a standardised scale for comparing potential exposure across taxa.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eClassification scale for exposure score interpretation.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScore range\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eExposure level\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEcological interpretation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePotential impact\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0.75\u0026ndash;1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVery high\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStrong ecological and spatial similarity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLikely major impact\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0.50\u0026ndash;0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSignificant niche overlap\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModerate to high impact\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0.25\u0026ndash;0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePartial interaction or occasional overlap\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLimited impact\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0.00\u0026ndash;0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEcological segregation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNegligible impact\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eEthics statement\u003c/h2\u003e\u003cp\u003eAll experimental procedures related to this study were carried out following institutional and national ethical guidelines for health sciences research in Burkina Faso. The study protocol received review and approval from the Institutional Ethics Committee of the Institut de Recherche en Sciences de la Sant\u0026eacute;, under approval number 32-2022/CEIRES, on May 27, 2022.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eData and code availability statement\u003c/h2\u003e\u003cp\u003eData are provided within the manuscript or supplementary information files S2 and S4. R Scripts or codes for correlation analyses and exposure score calculation are provided in supplementary files S3 and S5.\u003c/p\u003e\u003c/div\u003e\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank Daniel Fabrice SANON for his support during the mosquito’s DNA extraction. Special thanks to Seydou DRABO and Djamal Youssouf TOE for advising me while analysing the data. Many thanks to Séni ILBOUDO, Guel ZILA, Noufou DIABATE, Ibrahim DIABATE, Abdou Rasmané KABRE, and Ali OUARI for assisting us with our field and laboratory activities. We are grateful to Ousmane OUEDRAOGO, who assisted with the measurement of the larval physicochemical parameters.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is supported by a Wellcome Trust grant (UNS1285360).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDesign and implementation of the study: I.T, H.M, and A.D. Laboratory activities: I.T and A.J.L. Analysis and interpretation of data: I.T, K.M, A.K, J.B and H.M; Critical writing and revision of the article to extract its important intellectual content: I.T, K.M, A.J.L, J.B, N.T, A.K, A.M,J.K, A.D, and H.M,. Final approval of the version to be submitted: I.T, K.M, A.J.L, J.B, N.T, A.K, A.M, J.K, A.D, and H.M.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declare we have no competing interests. The funders were not involved in the design of the study, the collection, analysis, or interpretation of the data, the writing of the manuscript, or the decision to publish the results.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCDC. 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Null model analysis of species associations using abundance data. \u003cem\u003eEcology\u003c/em\u003e \u003cb\u003e91\u003c/b\u003e, 3384\u0026ndash;3397 (2010).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Anopheles coluzzii, Gene drive, Non-target organism, Ecological exposure score, environmental monitoring","lastPublishedDoi":"10.21203/rs.3.rs-8165654/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8165654/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn Burkina Faso, Target Malaria project developing gene drive technology targeting \u003cem\u003eAnopheles coluzzii\u003c/em\u003e raises important ecological questions about potential non-target effects. Understanding interactions in mosquitoes' natural environments is crucial for developing effective post-release environmental safety monitoring. This study assesses the ecological exposure and potential risks to non-target organisms associated with \u003cem\u003eAn. coluzzii\u003c/em\u003e suppression. Using co-occurrence, niche overlap metrics, and characterisation of physicochemical parameters, we evaluated interspecific relationships among mosquitoes and macroinvertebrate taxa from larval habitats in Burkina Faso. Combined index revealed distinct ecological relationships, ranging from competitive or facilitative coexistence to spatial segregation driven by predation or behavioural avoidance. Based on these interactions, an exposure score was developed to quantify the potential susceptibility of non-target organisms to ecological changes following the removal of \u003cem\u003eAn. coluzzii\u003c/em\u003e. The results showed variable exposure among taxa, with \u003cem\u003eAn. gambiae s.s.\u003c/em\u003e having the highest score, followed by \u003cem\u003eAn. arabiensis\u003c/em\u003e and \u003cem\u003eCulex\u003c/em\u003e spp. Predatory taxa such as Corixidae showed niche overlap but limited spatial co-occurrence, suggesting effective predation. The detection of hybrid forms (\u003cem\u003eAn. coluzzii\u003c/em\u003e x \u003cem\u003eAn. gambiae s.s.\u003c/em\u003e) further highlights the potential for gene flow. This study introduces a quantitative framework that combines ecological indices and exposure scores to predict potential risks to non-target organisms.\u003c/p\u003e","manuscriptTitle":"Ecological analysis of mosquito larval communities in Burkina Faso to inform environmental monitoring of genetic control programs","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-28 17:17:37","doi":"10.21203/rs.3.rs-8165654/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-08T07:38:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-06T17:23:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-05T11:51:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"282053980169470559653843237942421830769","date":"2025-12-03T19:05:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"31857789218752912031928167785217105954","date":"2025-12-03T13:25:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"338632208387493630130597040520938544752","date":"2025-12-02T11:38:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-24T13:10:54+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-24T12:53:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-21T12:53:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-21T12:53:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-11-20T14:24:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9fbd7096-2617-4ea4-857b-206274cb71b8","owner":[],"postedDate":"November 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":58565813,"name":"Biological sciences/Ecology"},{"id":58565814,"name":"Earth and environmental sciences/Ecology"},{"id":58565815,"name":"Biological sciences/Zoology"}],"tags":[],"updatedAt":"2026-01-19T16:45:03+00:00","versionOfRecord":{"articleIdentity":"rs-8165654","link":"https://doi.org/10.1038/s41598-026-35602-6","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-01-13 16:28:27","publishedOnDateReadable":"January 13th, 2026"},"versionCreatedAt":"2025-11-28 17:17:37","video":"","vorDoi":"10.1038/s41598-026-35602-6","vorDoiUrl":"https://doi.org/10.1038/s41598-026-35602-6","workflowStages":[]},"version":"v1","identity":"rs-8165654","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8165654","identity":"rs-8165654","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}