# In situ imagery unravels resources exploitation strategies among ant communities in a tropical agrosystem

Marie Bourel, Eva Faustin, Philippe Tixier, Bernard Raymond Abufera, Dominique Carval

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Marie Bourel, Eva Faustin, Philippe Tixier, Bernard Raymond Abufera, Dominique Carval. In situ imagery unravels resources exploitation strategies among ant communities in a tropical agrosystem. Ecosphere, 2025, 16 (11), pp.e70444. 10.1002/ecs2.70444. hal-05496566

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# In situ imagery unravels resources exploitation strategies among ant communities in a tropical agrosystem

Marie Bourel<fnref n="1" />,<fnref n="2" /> ID

Eva Faustin<fnref n="1" />,<fnref n="2" />

Philippe Tixier<fnref n="1" />,<fnref n="3" />

Bernard Raymond Abufera<fnref n="1" />

Dominique Carval<fnref n="1" />,<fnref n="2" />

## Correspondence

Dominique Carval

Email: dominique.carval@cirad.fr

## Funding information

European Regional Development Fund, Grant/Award Number: 2024-1248-005756; La Réunion through the EAFRD, Grant/Award Number: REU77071-1-000011

Handling Editor: Uffe N. Nielsen

## Abstract

Ants are one of the most widespread groups of insects, but the mechanisms allowing them to coexist despite having similar nutritional needs remain unclear. In this study, we investigated the structure and competitive dynamics of ant assemblages in pineapple agrosystems of La Réunion Island. Using an imagery approach that combined in-field image captures and a computer vision algorithm based on convolutional neural networks, we analyzed ant dominance hierarchies, foraging and recruitment behaviors, niche partitioning between day and night, as well as the impact of environmental variables. This approach provided highly detailed data on the strategies of individual species. Additionally, to better understand the resource exploitation strategies employed by ant species, we then tested the discovery-dominance trade-off. Our results revealed that *Pheidole megacephala* and *Solenopsis geminata* were the two dominant species, displaying high recruitment investment and frequent bait monopolization, particularly at night. In contrast, *Brachymyrmex cordemoyi* used a scout-based foraging strategy that allowed it to rapidly locate baits and maintain moderate dominance levels, during both day and night periods. With lower abilities to discover and control baits, subordinate species had to rely on alternative foraging strategies to persist. We found a significant positive correlation between the discovery and dominance abilities supporting a discovery-defense strategy rather than a discovery-dominance trade-off. This finding implies that resource monopolization in simplified ecosystems may benefit species optimizing both rapid resource location and competitive defense. Environmental factors, including plant species richness, plant cover, and mulch cover, influenced species abundance and occurrence, even though dominant species appeared to remain unaffected by habitat variations. The impacts of environmental factors highlight the role of habitat complexity in mediating competitive interactions shaping ant coexistence.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2025 The Author(s). *Ecosphere* published by Wiley Periodicals LLC on behalf of The Ecological Society of America.

<footnote n="1">¹GECO, Univ Montpellier, CIRAD, Montpellier, France</footnote>

<footnote n="2">²CIRAD, UPR GECO, Saint-Pierre, La Réunion, France</footnote>

<footnote n="3">³CIRAD, UPR GECO, Vientiane, Laos</footnote>
KEYWORDS

ant coexistence, artificial intelligence, computer vision, convolutional neural networks,
dominance, foraging strategies, habitat complexity, interspecific competition,
invasive species, La Réunion Island, resource partitioning, temporal data

INTRODUCTION

As one of the most diverse groups of insects, ants play a
central ecological role in ecosystems and are involved in
many biotic interactions (Hölldobler & Wilson, 1990;
Lach et al., 2009). Their presence affects several ecologi-
cal processes, such as the regulation of insect pest
populations (Anjos et al., 2022; Lutinski et al., 2024) or
soil engineering (Folgarait, 1998), and is essential for
maintaining ecosystem balance. However, their high
diversity and potential overlap in their nutritional needs
raise great interest in the mechanisms allowing them to
coexist and form long-term communities. Indeed, the
dynamics underpinning ant assemblage structure are
often complex and are moderated by the ecological strate-
gies of the constituent ant species (Gibb et al., 2023). A
species' ecological strategy defines how it acquires and
competes for resources (e.g., food, shelter), how it inter-
acts with both conspecific and interspecific individuals,
and how it responds to its environment. The ecological
strategy of a given species is influenced by morphological,
behavioral, and physiological traits, as well as trade-offs
between them. These traits are selected through envi-
ronmental pressures such as resource availability, com-
petition, disturbance or environmental stress (Arnan
et al., 2014; Gibb et al., 2023). Among these environ-
mental pressures, interspecific competition refers to
the interactions between two (or more) species, com-
peting for access to a limited resource (e.g., shelter or
food), in a shared environment. This type of competi-
tion can occur in two forms (Parr & Gibb, 2009): inter-
ference competition, where species directly and
aggressively interact with each other; and exploitation
competition, where species compete indirectly by
reducing resource availability. As a result, a species
can be either behaviorally dominant, by exhibiting
aggressive behaviors to control resources, numerically
dominant, by achieving a higher success in resource
acquisition through its abundance, or both. Dominant
species, through resource monopolization, can then
disrupt the structural organization of other competitive
species (Parr & Gibb, 2009), determining which species
can coexist in a given ecosystem. In response, subordi-
nate species can adapt by employing niche partitioning
and adjusting how they use their environment to
reduce direct competition and to coexist more

effectively within the same community (Salas-López
et al., 2022; Zuanon et al., 2025).

Several mechanisms have been proposed to explain
how multiple ant species can coexist in a shared environ-
ment. Among them, the variation in life history traits can
lead to differences in resource exploitation strategies. At
the worker level, for example, ant species may differ in
their thermal tolerance (Roeder et al., 2021) which influ-
ences their preferred foraging activity periods, as well as
in their body size, or in the size of specific body parts
(legs, clypeus, mandibles). At the colony level, ant species
may differ by their resource acquisition strategies
(e.g., individual or group foraging), colony reproduction
mode (dependent or independent colony founding),
degree of polygyny, polydomy, worker specialization, or
the mutualistic relationships in which they engage (Gibb
et al., 2023). Within a given ecological strategy (e.g., from
predation to honeydew farming), many traits are linked
through trade-offs. For instance, species with large man-
dibles for prey capture typically have a reduced clypeus,
limiting their ability to feed on liquid resources like hon-
ydew, and conversely (Gibb et al., 2023).

In ants, one of the most cited trade-offs that may
explain species coexistence is the discovery-dominance
trade-off (Fellers, 1987). In this hypothesis, some species
are more efficient at discovering a resource, whereas
others are better at controlling it. Since Fellers, this nega-
tive correlation between discovery and dominance abili-
ties has been debated. Evidence for this trade-off has
been found in Neotropical pastures, allowing *Pheidole
subarmata* (Mayr, 1884) and *Solenopsis geminata*
(Fabricius, 1804) to coexist (Perfecto & Vandermeer,
2011). Similarly, laboratory assays have confirmed this
trade-off in four invasive ant species (Bertelsmeier
et al., 2015). However, as opposed to this hypothesis,
studies on diverse Mediterranean ant assemblages
reported a positive correlation between discovery and
dominance (Castracani et al., 2014; Santini et al., 2007).
A similar conclusion was given in a study on Neotropical
arboreal ants, supporting the existence of a discovery-
defense strategy (Antoniazzi et al., 2021; Camarota
et al., 2018). The divergent correlations obtained in these
studies suggest that the mechanisms governing ant coex-
istence are context-dependent. This highlights the need
to consider multiple ecological factors in the same assem-
blage. For example, Parr and Gibb (2012) found in a
meta-analysis that the discovery-dominance trade-off occurred only in studies where parasitoids were present, whereas in simpler habitats, the two abilities were positively correlated.

As simplified ecosystems, agrosystems offer a valuable framework for investigating the mechanisms linking resource discovery and control, thereby contributing to our understanding of more complex ecosystems. In agrosystems, resource scarcity can intensify competition (Neumann & Pinter-Wollman, 2022), whereas their high levels of disturbance can promote the colonization by invasive species (Achury et al., 2020; Philpott et al., 2009). Additionally, invasive species are known for their ability to exploit disturbed environments and to outcompete and displace native species (Lach & Hooper-Bùi, 2009; LeBrun et al., 2012). Furthermore, these invasive species usually form polygynous and polydomous colonies (Tsutsui & Suarez, 2003), enabling them to invade and control disturbed ecosystems.

La Réunion Island, a French overseas territory located in the southwest of the Indian Ocean, is a valuable illustration of the mechanisms driving ecological shifts in disturbed environments. Human activities, including agriculture and urbanization, have drastically altered natural habitats, creating favorable conditions for the establishment of opportunistic introduced species. As a consequence, the island's ant communities are characterized by a high proportion of exotic invasive ant species (Blard et al., 2003; Nève De Mévergnies et al., 2024) and only one endemic ant species, *Camponotus aurosus* (Roger, 1863) (AntWeb, 2025). Pineapple cultivation is the major fruit crop of La Réunion (Agreste, 2023), with highly simplified and homogeneous habitats (Nurbel et al., 2021). As a result, they provide an occasion to investigate how ants coexist in disturbed environments. However, despite the ecological and economic importance of pineapple agricultural systems, the structure and dynamics of ant communities within these systems remain largely unstudied.

In the last decade, deep learning in computer vision has led to the introduction and the development of innovative methods for studying the ecology of arthropods (Høye et al., 2021). For instance, to monitor the abundance-activity of flower-visiting insects, Schneider et al. (2023) used a combination of camera traps and deep learning computer vision. Such techniques can also be used to estimate arthropod abundance, diversity, and biomass (Schneider et al., 2022). For this purpose, using prey sentinels and imagery, Tresson et al. (2019) developed the Corigan pipeline to reconstruct the trophic and non-trophic interaction network in coffee agricultural ecosystems in Costa Rica, with a focus on ants. Here, we introduced the application of a standardized

methodology that combines camera traps with computer vision analysis, allowing us to obtain a large amount of data, at a very fine temporal scale, and at the species or genus level.

Using innovative data collection methods, the present study aimed at (1) defining the structure of ant communities in pineapple agrosystems in La Réunion; (2) investigating niche partitioning regarding day and night shifts; (3) analyzing the different strategies employed by species to exploit resources; (4) testing the discovery-dominance trade-off and the discovery-defense strategy; and (5) examining environmental factors that may influence these mechanisms.

# METHODS

## Data acquisition

### Study sites

This study was conducted on 10 conventional agricultural plots in the south of the island, which is the main pineapple production area. It took place between April 7 and June 14, 2023, corresponding to the end of summer and the beginning of winter. Plot characteristics are given in Appendix S1: Section S1: Table S1.

### Plant and ant community surveys

On each plot, we defined quadrats of 25 m² (5 m × 5 m, 72 traps in total), spaced at least 15 m apart and uniformly distributed. In each quadrat, we recorded all plant species present and assigned them a plant cover based on a visual notation method with scores ranging from 1 (low cover) to 9 (complete cover) (Mathieu & Marnotte, 2000; Négrier et al., 2023). We also estimated mulch cover (i.e. litter layer using the same 1-9 scoring scale).

Additionally, a camera trap was placed at the center of each quadrat. The camera was fixed on a tripod (Manfrotto BeFree Advanced), oriented toward the ground, with a modified umbrella above for protection from both rain and sun. To provide a standardized image background, we installed a ceramic tile flush with the ground to maintain substrate continuity, at a distance of 10 cm from the camera lens (Panasonic DMC-FZ 300). In each picture, the captured area of the tile measured 182.52 cm² (15.6 cm × 11.7 cm). To determine the hierarchical structure of the ant community (i.e., dominant, subdominant, and subordinate species), we monitored their abundance after the addition to the ceramic tiles of a bait consisting of a central and linear deposit of honey
(Bertelsmeier et al., 2015) of 8 cm × 1 cm, resulting in an area of 8 cm². We performed two series of images at 30-s intervals (144 series in total): a daytime series from 11 a. m. to 2 p.m. (when the sun is at its zenith) and a nighttime series from 6 p.m. to 9 p.m. (6 p.m. corresponding to dusk). Additionally, samples were collected around each camera to confirm the identity of the species present using a binocular microscope (ZEISS Stemi 508) and identification keys for the genus (Fisher & Bolton, 2016) and species (Bolton, 2007; Fischer & Fisher, 2013; Guerrero, 2018; Hita Garcia & Fisher, 2015; LaPolla & Fisher, 2014; LaPolla, Hawkes, & Fisher, 2011; Mackay & Pacheco, 2013; Ortiz-Sepulveda et al., 2019) levels.

## Computer vision algorithm

In order to create the training and validation dataset, 1377 field images were manually labeled using the LabelImg software (Tzutalin, 2015) to capture the maximum diversity found in the plots. We completed this dataset with 187 images of infrequent species or castes, taken in the laboratory, to obtain a more balanced dataset. A total of nine species, belonging to three subfamilies (Formicinae, Dolichoderinae, and Myrmicinae), were identified in this study: *Brachymyrmex cordemoyi* (Forel, 1895), *Nylanderia bourbonica* (Forel, 1886), *Paratrechina longicornis* (Latreille, 1802), *Tapinoma melanocephalum* (Fabricius, 1793), *Technomyrmex albipes* (Fr. Smith, 1861), *Technomyrmex difficilis* (Forel, 1892), *Pheidole megacephala* (Fabricius, 1793), *Solenopsis geminata* (Fabricius, 1804), and *Tetramorium bicarinatum* (Nylander, 1846). Monomorphic species were represented by a single class, whereas *P. megacephala* was separated into two castes (minor and major). Due to the absence of major workers, *S. geminata* was represented only by minors of varying sizes, reflecting its polymorphism (Wills et al., 2018). We grouped the two *Technomyrmex* species to the genus level as it was impossible to differentiate the four species present in the island (*T. albipes*, *T. difficilis*, *Technomyrmex pallipes* (Smith, 1876), and *Technomyrmex vitiensis* (Mann, 1921)) on images, one of the discriminating criteria being the number and location of setae on the dorsal head, the pronotum, the mesosome, and the propodeal declivity (Bolton, 2007). Individuals of the Araneae and Blattodea orders and of the Gryllidae family were also labeled to avoid confusion with ants.

A modified version of the Corigan pipeline (Tresson et al., 2019) was then developed for the detection and identification of small individuals with Yolov4 (Bochkovskiy et al., 2020) as a core. First, we filtered the training and validation sets using the Laplacian variance method with a threshold of 12 to discard blurry images.

From the training dataset, 1200 sharp images were retained and six blurry images were discarded. From the validation dataset, 354 sharp images were kept and four blurry images were discarded. Next, for the training set, this pipeline slices images into image snippets (slice dimensions: width = 480 pixels, height = 480 pixels) to prevent any resolution loss at the entry of the Yolo convolutional neural network, which was set at the slice dimension. A window overlap ratio of 20% (overlap = 0.2) was applied between adjacent slices. This ensured that each object in the image was fully captured in at least one slice, reducing the likelihood of objects being truncated at the slice boundaries. The effective stride for slicing was calculated as stride = (1 - overlap) × slice dimension. An objectness threshold of 0.4 was used to retain bounding boxes during slicing. This threshold represents the minimum fraction of an object's bounding box area that must overlap with the slicing window (measured as Intersection over Union, IoU). Bounding boxes with an IoU below this threshold were excluded from the slice to ensure that only sufficiently represented objects were retained for downstream analysis. The image snippets without bounding boxes were discarded, which resulted in 33,046 image snippets for training. The training was performed for 30,000 iterations with a batch size of 32 (29 epochs). To test the trained model, we performed the same slicing process on the validation dataset without the no-label discarding step, leading to 13,816 image snippets. Detections on the image snippets were mapped back into the original images discarding detections on snippets which had a prediction confidence <0.8, with an IoU of 0.5. To address potential duplicates resulting from overlapping slices, a non-maximum suppression (NMS) approach was applied, using an IoU threshold of 0.5 to eliminate redundant detections of the same individual. Finally, classical metrics of computer vision were computed (Table 1).

We performed all neural network-related computations on a HP Z1 G9 Tower Desktop (Intel i9-12,900 processor, 128 Go RAM 4800 MHz) with a Nividia GeForce RTX 3070 graphical processing unit with 8 GB of memory. We used Ubuntu 22.04 LTS OS with CUDA 11.5 and CUDNN 9 to run the graphical processing unit. The pipeline was coded in Python 3.10. Object detection was performed within the Darknet framework using OpenCV 4.5.4.

## Dominance and foraging strategies

### Dominance analysis

Based on criteria adapted from the literature (Baccaro et al., 2010; Bestelmeyer, 2000; Carval et al., 2016), we
<table><caption>TABLE 1 Description of the algorithm metrics.</caption><thead><tr><th>Metric</th><th>Description</th></tr></thead><tbody><tr><td>Ground Truth (GT)</td><td>The no. individuals in all tested pictures.</td></tr><tr><td>True Positive (TP)</td><td>The no. individuals detected and assigned to the right class.</td></tr><tr><td>False Positive (FP)</td><td>The no. individuals detected but assigned to the wrong class.</td></tr><tr><td>False Negative (FN)</td><td>The no. individuals not detected.</td></tr><tr><td>Precision (TP/TP+FP)<sup>a</sup></td><td>The proportion of correct predictions considering all detections.</td></tr><tr><td>Recall (TP/TP+FN)<sup>a</sup></td><td>The proportion of individuals detected.</td></tr><tr><td>F1 (2×Precision ×Recall/ Precision + Recall) = TP/TP + 1/2(FN + FP)<sup>a</sup></td><td>The proportion of correct predictions.</td></tr><tr><td>Average Precision (AP)<sup>a</sup></td><td>The average precision for each class.</td></tr><tr><td>Mean Average Precision (mAP)<sup>a</sup></td><td>The average precision for all classes.</td></tr></tbody></table>

defined the following dominance-subordinate classification. A species was considered dominant (or respectively subdominant) when it met all of the following criteria: a frequency of presence on baits ≥10% (≥10%), an average abundance ≥20 (≥10%) on pictures where it was present, and a frequency of bait control ≥25% (≥20%) when it was present on pictures. Otherwise, the species was classified as subordinate. A species was considered to control a bait either when the number of individuals of this species was greater than 20, and it was the only species present, or when the number of individuals of this species was at least two times greater than the number of individuals of the second most abundant species, with a total number of individuals greater than 20.

## Foraging strategy and worker investment

To characterize the foraging strategies through the worker investment of each species, only the bait series where it occurred at least once was analyzed. We computed their average bait discovery time (BDT) as the mean time of arrival of a species on a series. We also estimated the average maximum of recruitment (MR) for

each species, which corresponds to the mean of the maximum number of workers recruited on a series. For this last index, a minimum threshold of five individuals (Santini et al., 2007) on a picture was set to consider that the species was recruiting on the series. Additionally, to test for differences in BDT and MR between the day and the night, we performed a Wilcoxon-Mann-Whitney test for each species.

## Discovery ability index versus dominance index and dominance transition

In order to measure a species' efficiency in locating baits, the discovery ability index (DAI) was computed as the ratio between the number of baits where a species arrived first and the total number of visited baits (Fellers, 1987; Santini et al., 2007). We then estimated the ability to monopolize baits with the dominance index (DI), computed as the percentage of control of the bait (based on the same criteria described in *Dominance analysis*) per series when the species was present. To assess the relationship between DAI and DI, the correlation coefficient (ρ) and the associated *p* value were estimated using Spearman's rank correlation test. Finally, we identified and reported all of the transitions of dominance from one species to another in series.

## Environmental factors influencing foraging activity

To study the effect of environmental variables on the abundance and occurrence of each species on our baits, we performed generalized linear mixed models (GLMM) using the *glmmTMB* package (Brooks et al., 2017) only on the quadrat where the species occurred. Two separate models were applied to each species, one for its abundance as the response variable with a negative binomial distribution, the other for its occurrence as the response variable with a binomial distribution. We defined the day/night period, species richness, plant species coverage, and mulch coverage as explanatory variables, with the quadrat and the plot as nested random effects. Next, we compared full models to nested models to test the significance of random effects using Akaike information criterion (AIC) criteria and ANOVA tests. Once the random effect structure was selected, the significance of the explanatory variables was tested through a backward selection process (Zuur et al., 2009) using the *drop1* function. All tests were performed at an alpha level of 0.05. To validate the selected models, we checked the dispersion and distribution of the residuals. For models on the

<footnote>ªAll these values can vary between 0 and 1, the best models being closest to 1. Variation depends on the confidence threshold for the prediction, below which the prediction is not considered correct, and removed from the final prediction set.</footnote>
abundance of each ant species, if overdispersion was detected, we followed the same selection process but used a Tweedie response distribution instead (Dunn & Smyth, 2008).

All the data analysis was performed using R version 4.2.2 (R Core Team, 2022), and all statistical tests were performed at an alpha level of 0.05.

# RESULTS

## Data acquisition—Detection of species by the modified Corigan pipeline

All data corresponding to the pipeline training results are presented in Appendix S1: Section S2. Regarding our pipeline training, the loss reached a value of approximately 3, and a mean average precision (mAP) of 97.3%. On the test dataset, using a confidence threshold of 0.8 and an IoU of 0.5, the pipeline reached a precision of 95%, a recall of 73%, an F1-score of 82%, and a mAP of 81%, indicating a good overall performance for species identification. For training, six classes had a large number of examples (>1000; Table 2). Conversely, there were only 203 examples of *N. bourbonica*. All taxa demonstrated a precision greater than 0.90 except for *N. bourbonica*, *T. melanocephalum*, and *Blattodea*, which showed a precision of 0.79, 0.88, and 0.85, respectively. The recall remained higher than 0.70 for all taxa except for *S. geminata* and *T. melanocephalum*, with a recall of 0.54 and 0.47, respectively. According to the confusion matrix (Appendix S1: Section S2: Figure S6), misidentifications and species confusions between species were minimal. This trained model was used to analyze all the images issued from our field experiment.

## Dominance and foraging strategies

### Dominance analysis

We obtained 20,666 and 22,125 images from our day and night bait experiments, respectively.

According to our criteria, *P. megacephala* and *S. geminata* were classified as dominant species, *B. cordemoyi* as subdominant, and the other species as subordinate (Table 3, Appendix S1: Section S3: Figure S9). *Pheidole megacephala* showed a strong difference in mean abundance between day and night (17.0 and 70.8, respectively), to the extent that it could be classified as subdominant during the day. This difference was less marked for *S. geminata* (30.7 and 59.0, respectively). *Brachymyrmex cordemoyi* was the most frequent species at baits with a particularly high frequency (46.1%). *Paratrechina longicornis* and *Technomyrmex* spp. showed a relatively high mean abundance (19.8 and 17.1, respectively) and frequency of bait control (34.0% and 43.0%, respectively) when they were present but were relatively infrequent at baits (5.5% and 2.3%, respectively), preventing them from being classified as subdominant. Differences between day and night in controlled baits were especially striking for *Technomyrmex* spp., which controlled 0% of baits during the day with an increase of 42.4% at night. Conversely, an opposite trend was observed for *T. bicarinatum*, whose bait control frequency rose from 6% at night to 20.7% during the day.

### Foraging strategy and worker investment

The analysis of foraging strategy and worker investment revealed different strategies, modulated according

<table><caption>TABLE 2 Metrics for each class with a confidence threshold at 0.8.</caption><thead><tr><th>Species</th><th>Precision</th><th>Recall</th><th>F1</th><th>AP</th><th>TP</th><th>FP</th><th>FN</th><th>No. train examples</th><th>No. test examples</th></tr></thead><tbody><tr><td>Aranea</td><td>0.90</td><td>0.94</td><td>0.92</td><td>0.93</td><td>47</td><td>5</td><td>3</td><td>216</td><td>50</td></tr><tr><td>Blattodea</td><td>0.85</td><td>0.75</td><td>0.79</td><td>0.74</td><td>50</td><td>9</td><td>17</td><td>190</td><td>67</td></tr><tr><td>Brachymyrmex cordemoyi</td><td>0.93</td><td>0.90</td><td>0.92</td><td>0.87</td><td>1888</td><td>146</td><td>200</td><td>5230</td><td>2088</td></tr><tr><td>Gryllidae</td><td>0.97</td><td>0.95</td><td>0.96</td><td>0.93</td><td>169</td><td>6</td><td>9</td><td>743</td><td>178</td></tr><tr><td>Nylanderia bourbonica</td><td>0.79</td><td>0.90</td><td>0.84</td><td>0.95</td><td>55</td><td>15</td><td>6</td><td>203</td><td>61</td></tr><tr><td>Paratrechina longicornis</td><td>0.96</td><td>0.83</td><td>0.89</td><td>0.81</td><td>1286</td><td>48</td><td>272</td><td>4646</td><td>1558</td></tr><tr><td>Pheidole megacephala major</td><td>0.93</td><td>0.97</td><td>0.95</td><td>0.95</td><td>102</td><td>8</td><td>3</td><td>387</td><td>105</td></tr><tr><td>Pheidole megacephala minor</td><td>0.97</td><td>0.77</td><td>0.86</td><td>0.77</td><td>2535</td><td>78</td><td>765</td><td>7853</td><td>3300</td></tr><tr><td>Solenopsis geminata</td><td>0.96</td><td>0.54</td><td>0.69</td><td>0.53</td><td>2327</td><td>103</td><td>2011</td><td>10,307</td><td>4338</td></tr><tr><td>Tapinoma melanocephalum</td><td>0.88</td><td>0.47</td><td>0.61</td><td>0.48</td><td>146</td><td>20</td><td>166</td><td>1817</td><td>312</td></tr><tr><td>Technomyrmex spp.</td><td>0.99</td><td>0.95</td><td>0.97</td><td>0.96</td><td>602</td><td>9</td><td>29</td><td>1205</td><td>631</td></tr><tr><td>Tetramorium bicarinatum</td><td>0.97</td><td>0.76</td><td>0.85</td><td>0.80</td><td>152</td><td>4</td><td>49</td><td>986</td><td>201</td></tr></tbody></table>
to night and day (Figure 1; Appendix S1: Section S3: Figure S8). During the night, *S. geminata* and *P. megacephala* discovered the baits relatively quickly (26 ± 5 and 31 ± 7 min, respectively; mean ± SE) and were characterized by a high MR (109 ± 17 and 106 ± 19 workers, respectively). *Paratrechina longicornis* and *Technomyrmex* spp. also had a relatively high mean MR (90 ± 21 workers and 66 ± 3 workers, respectively) but showed a greater BDT (69 ± 9 min and 54 ± 7 min,

respectively) than *S. geminata* and *P. megacephala*. Conversely, species such as *T. bicarinatum* and *N. bourbonica* were characterized by a fast to intermediate mean BDT (42 ± 9 min and 40 ± 7 min, respectively), but a much lower MR (21 workers and 16 ± 4 workers, respectively). In contrast, *B. cordemoyi* stood out with a particularly low BDT (18 ± 4 min), and an intermediate MR (45 ± 11 workers). Finally, *T. melanocephalum* did not recruit at night in our experiments.

<table><caption>TABLE 3 Hierarchical classification of ant species identified in our pictures.</caption><thead><tr><th rowspan="2">Species</th><th colspan="3">Bait frequency (%)</th><th colspan="3">Mean abundance when present</th><th colspan="3">Frequency of bait control<sup>a</sup> (%) when present</th></tr><tr><th>Global</th><th>Day</th><th>Night</th><th>Global</th><th>Day</th><th>Night</th><th>Global</th><th>Day</th><th>Night</th></tr></thead><tbody><tr><td>Dominant</td><td>≥5</td><td></td><td></td><td>≥20</td><td></td><td></td><td>≥25</td><td></td><td></td></tr><tr><td><i>Pheidole megacephala</i></td><td>21.1</td><td>13.3</td><td>28.3</td><td>54.4</td><td>17.0</td><td>70.8</td><td>53.8</td><td>25.6</td><td>65.7</td></tr><tr><td><i>Solenopsis geminata</i></td><td>19.6</td><td>14.5</td><td>24.4</td><td>48.9</td><td>30.7</td><td>59.0</td><td>56.1</td><td>43.9</td><td>62.5</td></tr><tr><td>Subdominant</td><td>≥10</td><td></td><td></td><td>≥10</td><td></td><td></td><td>≥20</td><td></td><td></td></tr><tr><td><i>Brachymyrmex cordemoyi</i></td><td>46.1</td><td>54.0</td><td>38.8</td><td>17.9</td><td>14.4</td><td>22.5</td><td>27.8</td><td>25.0</td><td>30.2</td></tr><tr><td>Subordinate</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td><i>Paratrechina longicornis</i></td><td>5.5</td><td>6.5</td><td>4.6</td><td>19.8</td><td>23.8</td><td>14.5</td><td>34.7</td><td>42.9</td><td>23.3</td></tr><tr><td><i>Nylanderia bourbonica</i></td><td>4.6</td><td>4.9</td><td>4.4</td><td>3.9</td><td>3.9</td><td>3.8</td><td>0.9</td><td>0.9</td><td>0.9</td></tr><tr><td><i>Technomyrmex</i> spp.</td><td>2.3</td><td>0.1</td><td>4.4</td><td>17.1</td><td>1.1</td><td>17.4</td><td>43.0</td><td>0.0</td><td>42.4</td></tr><tr><td><i>Tetramorium bicarinatum</i></td><td>2.4</td><td>1.7</td><td>3.0</td><td>9.1</td><td>9.5</td><td>8.9</td><td>11.2</td><td>20.7</td><td>6.0</td></tr><tr><td><i>Tapinoma melanocephalum</i></td><td>1.1</td><td>2.0</td><td>0.2</td><td>4.4</td><td>4.8</td><td>1.0</td><td>1.3</td><td>1.5</td><td>0.0</td></tr></tbody></table>

<figure><img src="image_2.png" /><figcaption>FIGURE 1 Investment strategies of ant species on baits depending on (A) the day and (B) the night. Bait discovery time (BDT) corresponds to the mean time of arrival of a species on a series. Maximum recruitment (MR) corresponds to the mean of the maximum number of workers recruited on a series with a minimum of 5. Error bars represent the SEs.</figcaption></figure>

<footnote><sup>a</sup>A species was considered to control a bait if it presented more than 20 individuals at a time on a bait and was the only species present, or if its number of individuals was at least twice that of other species. Values matching with thresholds are indicated in boldface.</footnote>
During the day, *S. geminata* and *P. megacephala* displayed an almost twice greater BDT (57 ± 8 and 59 ± 10 min, respectively) than during the night. These species displayed lower MR during the day, especially for *P. megacephala* (47 ± 12 workers). *Brachymyrmex cordemoyi* showed a similar MR compared to the night (39 ± 5 workers) but showed a higher BDT (29 ± 4 min). *Tetramorium bicarinatum* showed a similar BDT compared to the night (39 ± 11 min) but a higher MR than during the night (35 workers). Conversely, the BDT of *N. bourbonica* increased (61 ± 7 min), whereas its MR was similar compared to the night (12 ± 4 workers). *Paratrechina longicornis* showed a foraging strategy and worker investment similar to the night as compared to the night (BDT: 74 ± 8 and MR: 78 ± 20 workers), allowing it to reach an MR equivalent to *S. geminata* (83 ± 15 workers). *Tapinoma melanocephalum* was distinguished by a relatively low MR (19 ± 4 min) as well as a notably high BDT (99 ± 13 min). Finally, *Technomyrmex* spp. did not recruit during the day.

Statistical comparisons using the Wilcoxon Mann-Whitney test revealed significant differences in BDT between day and night for *S. geminata*, *B. cordemoyi*, and *N. bourbonica*, with higher median BDT during the day (Table 4). No significant differences were found for *P. longicornis* or *P. megacephala* (Table 4). Regarding MR, no significant changes were detected between day and night for any of the tested species (Table 4). Due to insufficient recruitment events, statistical comparisons could not be performed for *T. melanocephalum*, which recruited only during the day, *Technomyrmex* spp., which recruited only at night, and *T. bicarinatum*, which was observed recruiting only once per period.

## DAI versus DI and dominance transition

We found a significant positive correlation between DAI and DI for species across day, and night series (Figure 2; $\rho$ = 0.83 and 0.74; $p$ = 0.008 and 0.023, respectively).

Considering both day and night data, subordinate species generally maintained a low DI ranging from 0 to 3.3, with the exception of *P. longicornis*, which reached a DI of 7.1 ± 2.3 (mean ± SE) during the day. We observed a similar pattern for their DAI, ranging from 0% to 15%. During the day, *P. megacephala* and *S. geminata* exhibited similar intermediate DI values (12.2 ± 3.2 and 10.8 ± 3.0, respectively), whereas *P. megacephala* had a higher DAI than *S. geminata* (33.3% vs. 21.7%). However, at night, despite having similar DAIs to those observed during the day, *S. geminata* achieved a higher DI of 23.8 ± 4.8, whereas *P. megacephala* reached 36.2 ± 5.1. Notably, *B. cordemoyi* stood out with the highest DI and DAI during the day, the latter being more than twice that of the dominant species (DI = 16.3 ± 3.3, DAI = 59.4%), and maintained stable indices at night (DI = 14.4 ± 3.2, DAI = 53.1%).

Dominance transitions were observed in only 6 out of 144 series (Figure 3; Appendix S1: Section S3: Table S3). On average, dominance loss occurred after 94 ± 14 min. These transitions primarily involved *B. cordemoyi* and *S. geminata*, but also occurred between *N. bourbonica* and *T. bicarinatum*, as well as between *B. cordemoyi* and *P. megacephala*.

## Environmental factors influencing foraging activity

For the GLMM assessing the effects of environmental factors on species abundance, we used a negative binomial distribution, except for *T. melanocephalum* and *Technomyrmex* spp., for which a Tweedie distribution was applied. The results from GLMM showed that *B. cordemoyi*, *N. bourbonica*, *P. longicornis*, and *T. melanocephalum* were significantly more abundant and more frequent during the day (Tables 5 and 6). Conversely, *P. megacephala*, *S. geminata*, *T. bicarinatum*, and *Technomyrmex* spp. had a higher abundance and occurrence at night. The abundance and occurrence of

<table><caption>TABLE 4 Wilcoxon test results for bait discovery time and maximum recruitment between the day and the night.</caption><thead><tr><th rowspan="2">Species</th><th colspan="3">Bait discovery time</th><th colspan="3">Maximum recruitment</th></tr><tr><th>p</th><th>Day median (n)</th><th>Night median (n)</th><th>p</th><th>Day median (n)</th><th>Night median (n)</th></tr></thead><tbody><tr><td>Brachymyrmex cordemoyi</td><td><strong>0.002</strong></td><td><strong>13 (69)</strong></td><td><strong>5 (64)</strong></td><td>0.545</td><td>22 (43)</td><td>25 (34)</td></tr><tr><td>Nylanderia bourbonica</td><td><strong>0.017</strong></td><td><strong>44 (41)</strong></td><td><strong>21 (36)</strong></td><td>0.817</td><td>10 (4)</td><td>16 (2)</td></tr><tr><td>Paratrechina longicornis</td><td>0.650</td><td>65 (40)</td><td>54 (33)</td><td>0.784</td><td>74 (6)</td><td>80 (5)</td></tr><tr><td>Pheidole megacephala</td><td>0.112</td><td>25 (36)</td><td>15 (39)</td><td>0.117</td><td>36 (13)</td><td>93 (26)</td></tr><tr><td>Solenopsis geminata</td><td><strong>0.008</strong></td><td><strong>33 (46)</strong></td><td><strong>16 (48)</strong></td><td>0.144</td><td>86 (12)</td><td>141 (18)</td></tr></tbody></table>

Note: Values in boldface indicate a significant difference at an alpha level of 0.05.

Abbreviation: n, number of observations.
<figure><img src="image_3.png" /><figcaption>**FIGURE 2** Relationships between the discovery ability index and the dominance index (DI) depending on (A) the day and (B) the night. DI corresponds to the percentage of control of the bait per series when the species was present. Discovery ability index (DAI) corresponds to the ratio between the number of baits where a species arrived first and the total number of visited baits. Error bars represent the SEs.</figcaption></figure>

B. cordemoyi were positively correlated with species richness. The mulch cover had a positive effect on the abundance and occurrence of *N. bourbonica* as well as on those of *T. bicarinatum*, whereas it negatively affected the abundance of *T. melanocephalum*. The vegetal cover only negatively affected the abundance and occurrence of *N. bourbonica*.

## DISCUSSION

The introduction of new technologies has substantially improved ant community monitoring when compared to traditional approaches such as direct observation or pitfall traps. They allow for the precise (Collett & Fisher, 2017) and continuous monitoring of competitive interactions and resource exploitation strategies, with minimal disturbance to the organisms. By capturing detailed temporal and spatial patterns of ant behavior these methods provide unique insights into ecological dynamics and contribute to the development of more standardized and reproducible analyses. Here, the use of camera traps combined with a computer vision algorithm enabled the precise analysis of a considerable volume of data at the species level, providing highly detailed insights into the ecology of ant species within pineapple agroecosystems on La Réunion Island. This standardized method can be

used in routine and be easily adapted to other systems by adding other species and retraining the Yolo model used as the core of the pipeline. Nevertheless, although our method performed well in this study, its application in more diverse tropical communities, with higher species richness and morphological similarity, may be limited. One potential solution could be the implementation of hierarchical classification methods, such as those introduced by Tresson et al. (2021), which allow significant gains in the detection and classification metrics on an image dataset of small arthropods with subtle morphological distinctions. However, for species that differ by characteristics only visible through a stereomicroscope, as is the case in our study for the *Technomyrmex* species, achieving classification at the species level remains difficult. While this approach may entail significant equipment costs, recent technological developments have contributed to increasing its accessibility. Cost-effective alternatives, such as Raspberry Pi-based systems, low-cost imaging devices, and cloud-based GPU platforms, present viable solutions, particularly in contexts with limited resources. The combination of automated image analysis and camera trapping enabled us to identify distinct behavioral and competitive strategies among the ant species present in pineapple systems of La Réunion. By quantifying discovery times, recruitment intensity, and bait monopolization across day and night, we
<figure><img src="image_4.png" /><figcaption>**FIGURE 3** Transitions in ant species dominance at baits over time. A species was considered dominant at a bait if (1) its number of individuals was greater than 20 and it was the only species present, or (2) its number of individuals was at least twice that of the second most abundant species, with a total count greater than 20.</figcaption></figure>
<table><caption>TABLE 5 Results of the generalized linear mixed model (GLMM) of the environmental effects on species abundance.</caption><thead><tr><th>Species (n)</th><th>n</th><th>Fixed effect<sup>a</sup></th><th>Random effects</th><th>Estimate (SE)</th><th>LRT</th><th>p</th></tr></thead><tbody><tr><td rowspan="4">Brachymyrmex cordemoyi</td><td rowspan="4">42,791</td><td>Night</td><td rowspan="4">1|Plot/Quadrat</td><td>-0.804 (0.021)</td><td>1538.970</td><td>&lt;0.001</td></tr><tr><td>Vegetal Cover</td><td>-0.047 (0.024)</td><td>3.640</td><td>0.057</td></tr><tr><td>Species Richness</td><td>0.237 (0.055)</td><td>15.740</td><td>&lt;0.001</td></tr><tr><td>Mulch cover</td><td>0.018 (0.0)</td><td>1.370</td><td>0.243</td></tr><tr><td rowspan="4">Nylanderia bourbonica</td><td rowspan="4">30,952</td><td>Night</td><td rowspan="4">1|Quadrat</td><td>-0.396 (0.052)</td><td>57.464</td><td>&lt;0.001</td></tr><tr><td>Vegetal Cover</td><td>-0.061 (0.022)</td><td>7.312</td><td>0.007</td></tr><tr><td>Species Richness</td><td>-0.002 (0.051)</td><td>0.001</td><td>0.974</td></tr><tr><td>Mulch cover</td><td>0.042 (0.016)</td><td>6.366</td><td>0.012</td></tr><tr><td rowspan="4">Paratrechina longicornis</td><td rowspan="4">33,938</td><td>Night</td><td rowspan="4">1|Plot/Quadrat</td><td>-1.272 (0.080)</td><td>256.150</td><td>&lt;0.001</td></tr><tr><td>Vegetal Cover</td><td>-0.050 (0.020)</td><td>5.970</td><td>0.015</td></tr><tr><td>Species Richness</td><td>0.010 (0.048)</td><td>0.044</td><td>0.833</td></tr><tr><td>Mulch cover</td><td>-0.018 (0.011)</td><td>2.617</td><td>0.106</td></tr><tr><td rowspan="4">Pheidole megacephala</td><td rowspan="4">27,246</td><td>Night</td><td rowspan="4">1|Plot/Quadrat</td><td>2.689 (0.031)</td><td>57,840</td><td>&lt;0.001</td></tr><tr><td>Vegetal Cover</td><td>0.052 (0.051)</td><td>1.000</td><td>0.313</td></tr><tr><td>Species Richness</td><td>-0.121 (0.122)</td><td>0.900</td><td>0.332</td></tr><tr><td>Mulch cover</td><td>0.001 (0.029)</td><td>0.000</td><td>0.963</td></tr><tr><td rowspan="4">Solenopsis geminata</td><td rowspan="4">34,707</td><td>Night</td><td rowspan="4">1|Quadrat</td><td>1.599 (0.036)</td><td>1917.00</td><td>&lt;0.001</td></tr><tr><td>Vegetal Cover</td><td>-0.050 (0.033)</td><td>5.680</td><td>0.131</td></tr><tr><td>Species Richness</td><td>-0.034 (0.086)</td><td>0.160</td><td>0.691</td></tr><tr><td>Mulch cover</td><td>0.040 (0.020)</td><td>3.65</td><td>0.056</td></tr><tr><td rowspan="4">Tapinoma melanocephalum</td><td rowspan="4">15,241</td><td>Night</td><td rowspan="4">1|Plot/Quadrat</td><td>-3.391 (0.154)</td><td>658.170</td><td>&lt;0.001</td></tr><tr><td>Vegetal Cover</td><td>0.001 (0.022)</td><td>0.000</td><td>0.961</td></tr><tr><td>Species Richness</td><td>-0.055 (0.065)</td><td>0.710</td><td>0.401</td></tr><tr><td>Mulch cover</td><td>-0.046 (0.017)</td><td>6.340</td><td>0.012</td></tr><tr><td rowspan="4">Technomyrmex spp.</td><td rowspan="4">29,337</td><td>Night</td><td rowspan="4">1|Quadrat</td><td>5.245 (0.166)</td><td>1879.100</td><td>&lt;0.001</td></tr><tr><td>Vegetal Cover</td><td>-0.012 (0.017)</td><td>0.520</td><td>0.471</td></tr><tr><td>Species Richness</td><td>0.073 (0.050)</td><td>2.080</td><td>0.150</td></tr><tr><td>Mulch cover</td><td>0.008 (0.014)</td><td>0.340</td><td>0.561</td></tr><tr><td rowspan="4">Tetramorium bicarinatum</td><td rowspan="4">17,541</td><td>Night</td><td rowspan="4">1|Quadrat</td><td>0.306 (0.050)</td><td>38.881</td><td>&lt;0.001</td></tr><tr><td>Vegetal Cover</td><td>0.014 (0.029)</td><td>0.238</td><td>0.625</td></tr><tr><td>Species Richness</td><td>-0.043 (0.073)</td><td>0.346</td><td>0.556</td></tr><tr><td>Mulch cover</td><td>0.032 (0.015)</td><td>4.396</td><td>0.036</td></tr></tbody></table>

Abbreviations: LRT, likelihood ratio test; n, number of observations.

characterized species hierarchies and foraging patterns within the ant community.

In total nine species were identified in this study, all of them being invasive species: *B. cordemoyi*, *N. bourbonica*, *P. longicornis*, *T. melanocephalum*, *T. albipes*, *T. difficilis*, *P. megacephala*, *S. geminata*, and *T. bicarinatum*. We found that *S. geminata* and *P. megacephala* dominated these communities. Indeed, these aggressive species monopolized a high percentage of baits

when they were present, especially during the night. We classified *B. cordemoyi* as a subdominant species as its occurrence at baits was particularly high, during both the day and the night, and monopolized a relatively high percentage of baits. All the remaining species were classified as subordinate species although *P. longicornis* and *Technomyrmex* spp. showed a high frequency of monopolized baits when they were present. However, their frequency at baits was relatively

<footnote><sup>a</sup>For the Day/Night effect, the estimates are expressed relative to the night, which is taken as the reference modality. Degrees of freedom (df) for all tests were equal to 1. Values in boldface indicate a significant effect at an alpha level of 0.05.</footnote>
<table><caption>TABLE 6 Results of the generalized linear mixed model (GLMM) of the environmental effects on species occurrence.</caption><thead><tr><th>Species (n)</th><th>n</th><th>Fixed effect<sup>a</sup></th><th>Random effects</th><th>Estimate (SE)</th><th>LRT</th><th>p</th></tr></thead><tbody><tr><td rowspan="4">Brachymyrmex cordemoyi</td><td rowspan="4">42,791</td><td>Night</td><td rowspan="4">1|Plot/Quadrat</td><td>-1.112 (0.028)</td><td>1699.840</td><td>&lt;0.001</td></tr><tr><td>Vegetal Cover</td><td>-0.034 (0.022)</td><td>2.420</td><td>0.120</td></tr><tr><td>Species Richness</td><td>0.202 (0.049)</td><td>14.240</td><td>&lt;0.001</td></tr><tr><td>Mulch cover</td><td>0.014 (0.014)</td><td>1.020</td><td>0.313</td></tr><tr><td rowspan="4">Nylanderia bourbonica</td><td rowspan="4">30,952</td><td>Night</td><td rowspan="4">1|Quadrat</td><td>-0.369 (0.060)</td><td>38.585</td><td>&lt;0.001</td></tr><tr><td>Vegetal Cover</td><td>-0.060 (0.021)</td><td>7.499</td><td>0.006</td></tr><tr><td>Species Richness</td><td>0.001 (0.049)</td><td>0.000</td><td>0.983</td></tr><tr><td>Mulch cover</td><td>0.041 (0.015)</td><td>6.681</td><td>0.010</td></tr><tr><td rowspan="4">Paratrechina longicornis</td><td rowspan="4">33,938</td><td>Night</td><td rowspan="4">1|Plot/Quadrat</td><td>-1.694 (0.082)</td><td>616.360</td><td>&lt;0.001</td></tr><tr><td>Vegetal Cover</td><td>-0.049 (0.020)</td><td>5.650</td><td>0.017</td></tr><tr><td>Species Richness</td><td>0.021 (0.050)</td><td>0.170</td><td>0.676</td></tr><tr><td>Mulch cover</td><td>-0.016 (0.011)</td><td>1.970</td><td>0.161</td></tr><tr><td rowspan="4">Pheidole megacephala</td><td rowspan="4">27,246</td><td>Night</td><td rowspan="4">1|Plot/Quadrat</td><td>2.709 (0.055)</td><td>3557.600</td><td>&lt;0.001</td></tr><tr><td>Vegetal Cover</td><td>0.056 (0.049)</td><td>1.300</td><td>0.250</td></tr><tr><td>Species Richness</td><td>-0.114 (0.107)</td><td>1.000</td><td>0.312</td></tr><tr><td>Mulch cover</td><td>-0.010 (0.028)</td><td>0.100</td><td>0.723</td></tr><tr><td rowspan="4">Solenopsis geminata</td><td rowspan="4">34,707</td><td>Night</td><td rowspan="4">1|Plot/Quadrat</td><td>2.033 (0.050)</td><td>2078.700</td><td>&lt;0.001</td></tr><tr><td>Vegetal Cover</td><td>-0.043 (0.029)</td><td>2.190</td><td>0.139</td></tr><tr><td>Species Richness</td><td>-0.021 (0.077)</td><td>0.080</td><td>0.782</td></tr><tr><td>Mulch cover</td><td>0.034 (0.018)</td><td>3.480</td><td>0.062</td></tr><tr><td rowspan="4">Tapinoma melanocephalum</td><td rowspan="4">15,241</td><td>Night</td><td rowspan="4">1|Plot/Quadrat</td><td>-3.129 (0.176)</td><td>542.240</td><td>&lt;0.001</td></tr><tr><td>Vegetal Cover</td><td>-0.006 (0.020)</td><td>0.090</td><td>0.769</td></tr><tr><td>Species Richness</td><td>-0.083 (0.054)</td><td>2.290</td><td>0.131</td></tr><tr><td>Mulch cover</td><td>-0.020 (0.013)</td><td>2.560</td><td>0.109</td></tr><tr><td rowspan="4">Technomyrmex spp.</td><td rowspan="4">29,337</td><td>Night</td><td rowspan="4">1|Quadrat</td><td>5.154 (0.248)</td><td>1443.000</td><td>&lt;0.001</td></tr><tr><td>Vegetal Cover</td><td>-0.013 (0.015)</td><td>0.730</td><td>0.392</td></tr><tr><td>Species Richness</td><td>0.062 (0.045)</td><td>1.910</td><td>0.167</td></tr><tr><td>Mulch cover</td><td>0.006 (0.013)</td><td>0.190</td><td>0.659</td></tr><tr><td rowspan="4">Tetramorium bicarinatum</td><td rowspan="4">17,541</td><td>Night</td><td rowspan="4">1|Quadrat</td><td>0.759 (0.097)</td><td>64.264</td><td>&lt;0.001</td></tr><tr><td>Vegetal Cover</td><td>0.015 (0.030)</td><td>0.238</td><td>0.626</td></tr><tr><td>Species Richness</td><td>-0.044 (0.075)</td><td>0.346</td><td>0.556</td></tr><tr><td>Mulch cover</td><td>0.032 (0.015)</td><td>3.999</td><td>0.046</td></tr></tbody></table>

Abbreviations: LRT, likelihood ratio test; n, number of observations.

low as we observed these species in less than 6% of baits (Table 3).

Our results provide new insights into the resource exploitation strategies of each species. In this study, the BDT reflects the foraging behavior of the species, whereas the mean MR indicates the investment allocated by a species at the colony level to resource acquisition.

We observed both in *S. geminata* and *P. megacephala* a moderate to high BDT but a relatively high MR. This

suggests that these dominant species invest moderately in scout workers that search for new resources but invest heavily in foraging workers once the resource has been located. As a consequence, they displayed a high DI and a moderate DAI, respectively. This aligns with a previous study concluding that *S. geminata* was slower to discover and recruit to baits but was better at defending them (Axen et al., 2014). This could result from the polydomial nature of the colonies in both *P. megacephala* and

<footnote><sup>a</sup>For the Day/Night effect, the estimates are expressed relative to the night, which is taken as the reference modality. Degrees of freedom (df) for all tests were equal to 1. Values in boldface indicate a significant effect at an alpha level of 0.05.</footnote>
*S. geminata*, which may be an advantageous strategy (Robinson, 2014). Previous studies have shown that polydomy increases the number of foraging workers and shortens resource discovery time (Robinson, 2014) and may allow adaptation to disturbance in resource availability by modulating the colony's network components (Burns et al., 2021).

The subdominant ant species *B. cordemoyi* displayed a moderate MR and a low BDT, both during the day and during the night. This suggests that *B. cordemoyi* invests heavily in a foraging strategy based on numerous scouts that were widely scattered across most of our plots. This behavior likely explains its notably high DAI, since these scouts rapidly located new resources before beginning recruitment (Menzel et al., 2010). During the day, its DI exceeded that of dominant species, likely due to its higher foraging activity and faster BDT. The relatively low BDT and moderate MR observed in *B. cordemoyi* may be indicative of a polydomous nesting structure. Further molecular and behavioral studies are needed to assess polygyny and unicoloniality in *B. cordemoyi*. Field observations indicate that *B. cordemoyi* frequently follows recruitment trails of dominant species such as *P. megacephala* and *S. geminata* without eliciting aggression (M. Bourel, personal observation), suggesting potential mechanisms of coexistence. This behavior may result from trail sharing through the detection of chemical, visual, and tactile cues left by foragers of other species or through olfactory eavesdropping (Chalissery et al., 2019; Menzel et al., 2010).

*Paratrechina longicornis* and *Technomyrmex* spp. showed similar strategies with high BDT and MR values but low DAI and DI. In a study on behavioral strategies of invasive ant species, the polygynous and polydomous ant species *P. longicornis* was found not to be aggressive suggesting that it may be an exploitative competitor (Bertelsmeier et al., 2015). This could mean that its resource acquisition relies on the rapidity to locate food sources and on an efficient recruitment of nestmates. However, our results suggest another strategy since we measured relatively high BDT, exceeding an hour. Nevertheless, *P. longicornis* may achieve bait monopolization by recruiting a large number of nestmates in some locations. This high MR, associated with a low DI, suggests that in some cases, *P. longicornis* may behave as an interference competitor. Ants of the *Technomyrmex* genus are generally considered as arboreal or subarboreal ants, though some species nest in the ground (Wetterer, 2013). Species within the *T. albipes* group are both polygynous and polydomous. Among them, *T. albipes*, *T. difficilis*, and *T. vitiensis* are widely distributed invasive species, with *T. difficilis* having established itself as a dominant arboreal ant in certain regions of

Florida (Bolton, 2007; Wetterer, 2013). However, in our study, we classified the ants of the *Technomyrmex* genus as subordinate.

*Tapinoma melanocephalum* was observed recruiting exclusively during the day and exhibited a relatively long BDT and low maximum recruitment (MR). These results contrast with previous studies describing the species as an efficient food discoverer with rapid recruitment (Lu, 2012; Von Aesch & Cherix, 2001; Zhou et al., 2014). These discrepancies suggest that its foraging performance may be context-dependent, influenced by environmental conditions and competitive pressure. Its thermal tolerance (Appel et al., 2004) may allow diurnal activity when dominant species are less active (Bestelmeyer, 2000). The low MR might result from a recruitment strategy relying less on pheromone reinforcement and more on the role of initial discoverers (Möglich & Hölldobler, 1975; Von Aesch & Cherix, 2001). Furthermore, the species' pygidial gland secretions could serve as a chemical deterrent, reducing direct competition (Tomalski et al., 1987). Overall, *T. melanocephalum* may rely on opportunistic foraging and chemical defense rather than early discovery and mass recruitment to persist in competitive environments.

*Tetramorium bicarinatum* showed a MR similar to that of *B. cordemoyi* indicating moderate worker recruitment. During the day, its BDT was also comparable to that of *B. cordemoyi* but was higher at night. This species displayed low DI and DAI, meaning that, even if it is occasionally able to rapidly discover resources, it generally cannot compete with (sub)dominant species. This aligns with another experiment in which *T. bicarinatum* was identified as a subordinate species, controlling only 20% of baits where it was present and recruiting relatively few workers (Carval et al., 2022). However, in one case of our study, *T. bicarinatum* effectively took over a bait from *N. bourbonica* (Figure 3).

Finally, our results showed that *N. bourbonica* had the lowest MR and an intermediate BDT, contrasting with previous descriptions of *Nylanderia* species as efficient and rapid foragers (LaPolla, Brady, & Shattuck, 2011). While *N. bourbonica* has been described as a weak competitor against *S. invicta* (Roldán et al., 2020), other investigations indicate that it can persist with this species (Wetterer et al., 2007), as well as demonstrate aggressiveness, and even suppress it (Ni et al., 2024). This suggests that its competitive performance may differ between region and habitat.

The significant positive correlation between the DAI and the DI observed in our study supports the discovery-defense strategy rather than the discovery-dominance trade-off. Additionally, the exceptionally low cases of dominance transitions further indicate that once a
species establishes dominance at a resource, it is likely to keep it throughout bait consumption (Camarota et al., 2018). While trade-offs between discovery and dominance abilities are commonly expected, an increasing number of studies are inconsistent with this assumption. The finding of a discovery-defense strategy aligns with previous research on arboreal ants (Antoniazzi et al., 2021; Camarota et al., 2018; Santini et al., 2007), even though the present study involved ground-dwelling invasive ants in a simplified habitat. Similar patterns have been observed in ant communities within banana agrosystems, where a discovery-defense strategy appears to be at play (Carval et al., 2016). Furthermore, the ability to excel simultaneously at discovering and controlling resources of *S. geminata* and *P. megacephala* suggests that resource monopolization may reduce the viability of trade-offs, and favor strategies optimizing both discovery and defense capabilities. This conclusion is consistent with Davidson (1998) who proposed that the discovery-dominance trade-off can be bypassed by species with improved capacities to exploit carbohydrate resources, or by invasive species that no longer face their natural enemies or ecological constraints (Achury et al., 2020; Feener Jr., 2000). Likewise, the Argentine ant, *L. humile*, has been shown to outcompete native ants by quickly locating and monopolizing food resources (Holway, 1999). As a consequence, the discovery-defense strategy may represent a mechanism facilitating the coexistence of subordinate species within ant assemblages when they achieve being the first to dominate at a resource. This strategy could help maintain species diversity even in highly simplified ecosystems dominated by aggressive invasive species.

Interestingly, our results suggest that investment in worker recruitment and speed of discovery are not necessarily directly correlated with a higher DI, nor with a higher DAI. For example, *P. longicornis* demonstrated a high recruitment effort during the day, whereas its DAI was low. Similarly, *T. bicarinatum* discovered the baits relatively quickly during the day but its DAI was low compared to other species. These data suggest that other factors may influence dominance and resource acquisition success. For instance, species with strategies of nesting dispersion may benefit from having access to several resource sources. In addition, caste specialization for defense, and colonies with a large number of workers (Pearce-Duvet et al., 2011), such as those of *P. megacephala* and *S. geminata*, could contribute to their ability to monopolize resources. Morphological adaptations, including larger body size or armored cuticles, may strengthen a species' capacity to resist competitors and maintain dominance. Three aspects not explored in this study are the species' capacities to consume resources

after discovering them (Dejean et al., 2005), the effect of bait quality and the effect of their distance to the nests (Fewell et al., 1992), which could have a significant impact on dominance dynamics.

The results support the hypothesis that mechanisms governing ant coexistence, such as the discovery-defense strategy, are highly context-dependent. Significant disparities in DAI and DI were observed, particularly at night, when dominant species were overrepresented. Among them, *S. geminata* has already been shown to be more active during the night (Norasmah et al., 2006). During the day, dominant species ceded more space to subordinate species, which were more frequent and abundant at these times (except for *Technomyrmex* spp.). These findings are consistent with a study led in a forest ant assemblage where dominant species were prevalent during the night (Stuble et al., 2013). In our plots, we observed that some species showed temporal specialization. For example, *Technomyrmex* spp. only recruited at night, whereas *T. melanocephalum* was only active during the day. For these species, temporal niche partitioning may act as an additional coexistence strategy, by avoiding direct confrontations (Bestelmeyer, 2000). Other environmental parameters could be significant sources of variation in the outcomes of ant coexistence strategies. In our study, plant species richness had a positive effect on the abundance and occurrence of *B. cordemoyi*. This result is consistent with its frequent association with floral resources (Costa, 2016), as increased plant diversity could provide a greater availability of nectar and extrafloral nectaries. Mulch cover had a positive effect on the abundance and occurrence of *N. bourbonica* and *T. bicarinatum*, suggesting that these species benefit from the shelter and moisture provided by mulch. Previous observations in California demonstrated that *T. bicarinatum* prefers moist nesting sites (Martinez & Weis, 2011). Similarly, Carval et al. (2022) found that this species was more frequent and more abundant in banana plots with cover crops than in those with bare soil (Carval et al., 2022). In contrast, mulch cover had a negative effect on *T. melanocephalum*, potentially acting as a physical barrier that interferes with its foraging activity. Interestingly, vegetation cover had a negative effect on both the abundance and occurrence of *N. bourbonica*. One possible explanation is that vegetal cover might reduce ground temperature and limit open spaces, which could be less favorable for this species or could influence competitive interactions, indirectly shaping its distribution. Environmental factors, however, had no significant effect on the two dominant species, *P. megacephala* and *S. geminata*. This suggests that their high adaptability to disturbed habitats allows them to dominate regardless of variations in plant richness, mulch, or vegetation cover.
These findings highlight the importance of habitat complexity and resource availability in determining competitive interactions (Achury et al., 2020). Pineapple agrosystems, as simplified environments, align with predictions by Parr and Gibb (2012), who reported that reduced structural complexity improves the positive relationship between discovery ability and dominance (Parr & Gibb, 2012). This suggests that dominant ants may have an advantage in locating and controlling resources in simpler and disturbed environments, as they face fewer obstacles. Conversely, in more heterogeneous habitats, increased environmental variability can prevent resource monopolization by slowing down aggressive species recruitment and altering interspecific interactions (Sarty et al., 2006). Another possibility is that greater habitat complexity may promote niche diversification and provide a wider range of resources, decreasing the impact of interspecific competition. Future research should also investigate how seasonal changes impact ant activity and competition (Arnan et al., 2014), as well as the role of other biotic interactions, such as predation or mutualism, in shaping ant coexistence dynamics. These factors could also support the context-dependent mechanisms promoting coexistence strategies (Wiescher et al., 2011; Yitbarek et al., 2017).

Antoniazzi et al. (2021) and Parr and Gibb (2012) assessed dominance through rankings based on the percentage of baits first discovered (discovery) and the percentage of baits monopolized at the end of the experiment (dominance). In contrast, Perfecto and Vandermeer (2011) defined dominance based on the abundance of ants at bait (with a maximum of 20 individuals). Bertelsmeier et al. (2015) focused on interference competition, using killing and survival indices to measure dominance. Here, in the context of exploitation competition, we aimed at describing the strategies employed by our ant species by integrating multiple aspects, including their hierarchy based on numerical dominance and their foraging strategies as a result of their investment through recruitment efforts and BDTs. In addition, we examined the influence of different ecological contexts, such as the day and night effect, as well as environmental factors such as mulch cover and plant species diversity and coverage. These methodological differences can lead to variable outcomes and interpretations, emphasizing the need to carefully consider the criteria used to evaluate dominance in ant communities.

## AUTHOR CONTRIBUTIONS

**Conceptualization:** Marie Bourel, Dominique Carval, and Philippe Tixier. **Methodology:** Marie Bourel, Dominique Carval, and Philippe Tixier. **Software:** Dominique Carval.

**Formal analysis:** Marie Bourel, Eva Faustin, and Dominique Carval. **Investigation:** Marie Bourel, Eva Faustin, Bernard Raymond Abufera, and Dominique Carval. **Writing—original draft:** Marie Bourel and Dominique Carval. **Writing—review and Editing:** Marie Bourel, Eva Faustin, Philippe Tixier, and Dominique Carval. **Visualization:** Marie Bourel and Dominique Carval. **Supervision:** Dominique Carval and Philippe Tixier.

## ACKNOWLEDGMENTS

This work was carried out as part of the CIRAD DPP SADur agronomical research program. This program is co-funded by the European Union, the French State, and La Réunion Region. Europe is committed to La Réunion through the EAFRD (grant REU77071-1-000011), for which La Réunion Department is the managing authority, and through the ERDF (grant 2024-1248-005756), for which La Réunion Region is the managing authority. The authors thank Paulin Lenclu for the help with fieldwork, and all the producers who agreed to participate in this study.

## CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest.

## DATA AVAILABILITY STATEMENT

Data and R code (Bourel et al., 2025) are available from Figshare: https://doi.org/10.6084/m9 figshare.28694486.

## ORCID

Marie Bourel https://orcid.org/0009-0003-0225-4207

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## SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.

**How to cite this article:** Bourel, Marie, Eva Faustin, Philippe Tixier, Bernard Raymond Abufera, and Dominique Carval. 2025. "In Situ Imagery Unravels Resources Exploitation Strategies among Ant Communities in a Tropical Agrosystem." *Ecosphere* 16(11): e70444. https://doi.org/10.1002/ecs2.70444