# Giant Gardeners of the Forest: How African Forest Elephants (Loxodonta cyclotis) Shape Mammal Biodiversity in the Republic of Congo

Laura Versavaud, Nina Giotto, Paul Aczel, Gabriel Michelin, Jean-François Gerard

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Laura Versavaud, Nina Giotto, Paul Aczel, Gabriel Michelin, Jean-François Gerard. Giant Gardeners of the Forest: How African Forest Elephants (Loxodonta cyclotis) Shape Mammal Biodiversity in the Republic of Congo. African Journal of Ecology, 2025, 63 (3), pp.e70043. 10.1111/aje.70043. hal-05041876

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RESEARCH ARTICLE

OPEN ACCESS

# Giant Gardeners of the Forest: How African Forest Elephants (*Loxodonta cyclotis*) Shape Mammal Biodiversity in the Republic of Congo

Laura Versavaud¹ | Nina Giotto² | Paul Aczel² | Gabriel Michelin³ | Jean-François Gerard¹

¹Comportement et Écologie de la Faune Sauvage, INRAE, Castanet-Tolosan, France | ²HELP Congo, Pointe-Noire, Republic of the Congo | ³ZooParc de Beauval & Beauval Nature, Saint Aignan, France

**Correspondence:** Laura Versavaud (laura.versavaud@outlook.com)

**Received:** 5 December 2024 | **Revised:** 27 March 2025 | **Accepted:** 30 March 2025

**Funding:** This work was supported by Beauval Nature.

**Keywords:** camera traps | cascading effects | ecosystem engineer | megaherbivore | Shannon index | species richness | tropical rainforest

## ABSTRACT

The African forest elephant (*Loxodonta cyclotis*), a critically endangered species, is a megaherbivore that browses and transforms large quantities and varieties of vegetation during its foraging activities and movements. However, the cascading effects of this 'ecosystem engineer' on animal biodiversity are poorly known. In this study, based on data from 38 camera traps placed in a lowland forest in the National Park of Conkouati-Douli, Republic of Congo, we examined the effects of the forest elephant and ecological variables like flooding time on local biodiversity in mammal species. Camera traps recorded 31 terrestrial mammal species and our results show that mammal biodiversity was maximum for intermediate levels of frequentation by forest elephants. The occurrence frequency of various mammal species also reached its highest value under similar conditions. Possible explanations for this phenomenon are discussed.

## 1 | Introduction

The Congo Basin hosts the second largest tropical forest in the world and is one of the richest ecosystems on the planet, home to biodiversity hotspots and rare species (Brandon 2014; GCF and FAO 2024). However, in addition to climate change, the animal and plant communities living there are subject to anthropogenic pressures such as deforestation and land-use change, poaching, road and infrastructure construction, mining industry and urbanisation (Maisels et al. 2013; Potapov et al. 2017; Venter et al. 2016). All these pressures, but especially hunting and poaching, combined with climate change, affect the structure, diversity and interactions of animal and plant communities, and thus alter the functioning of ecosystems, then responsible for what is commonly known as ecological erosion (Abernethy et al. 2013; Poulsen et al. 2013; Wilkie et al. 2011). Populations of large mammals (≥ 5 kg) are the most affected by these threats,

notably because of their body size, the size of their home range, the value of their meat, their ability to conflict with rural livelihoods and their slow reproductive rate (Goheen et al. 2013). However, these mammals have ecologically relevant interactions and perform a variety of ecosystem functions such as seed dispersal and predation, herbivore population control, herbivory and nutrient recycling, all of which help to shape the rainforest (Wright et al. 2007). They have an important ecological role to play in the composition and diversity of vegetation species, some even influencing vegetation structure, carbon storage, seed recruitment and other ecosystem processes (Clark et al. 2012; Trepel et al. 2024).

Among all mammals, some have disproportionately large roles. This is the case of elephants, the largest extant terrestrial megaherbivores, which are ecosystem engineers and keystone species: they control the availability of resources for other species

<footnote>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.</footnote>

<footnote>© 2025 The Author(s). *African Journal of Ecology* published by John Wiley & Sons Ltd.</footnote>
by modifying their physical environment (Jones et al. 1994) and have consequent impacts on the ecosystems they frequent (Poulsen et al. 2018). They can influence the structure and composition of the vegetation and are thus able to alter the availability and distribution of food and refugia for other species (Tambling et al. 2013; Skarpe et al. 2004; Western 1989), transform the landscape of fear (Roux et al. 2018) or even modify trophic guild structures in savannahs (Fritz et al. 2002).

Scientific literature currently describes three geographically, morphologically and ecologically distinct species of elephants: the Asian elephant (Elephas maximus), the African savannah elephant (Loxodonta africana) and the African forest elephant (Loxodonta cyclotis). The three species are experiencing an unprecedented decline in numbers, mainly due to poaching, habitat reduction and fragmentation (Hankinson et al. 2020; Riddle et al. 2010). Forest elephants are suffering the most drastic decline (Underwood et al. 2013): they lost more than 80% of their headcounts over the last 40 years (Gobush et al. 2021) and in Central Africa, their numbers fell by 62% between 2002 and 2011, their current populations occupying less than 25% of their potential range (Maisels et al. 2013). Protected areas considered to be sanctuaries are not spared either, since it has been shown that in the Minkebe National Park in Gabon – renowned for having a high density of forest elephants – populations suffered a decline of 78 to 81% between 2004 and 2014 (Poulsen et al. 2017). Although the forest elephant is classified as Critically Endangered in the IUCN Endangered Species List, the species remains largely understudied compared to its savannah sister species (Breuer et al. 2016).

Some studies have pointed out the role of forest elephants as ‘gardeners’ of the tropical rainforest (Campos-Arceiz and Blake 2011). Indeed, with a body mass of up to 6 t, they have high nutritional requirements and must consume an average of 100 to 200 kg of food per day, and sometimes up to 450 kg daily (Frederick 2019). As generalists, they are able to consume more than 500 plant species (Blake 2002a) and thus affect vegetation structure and species composition due to the frequency-dependent grazing pressure they exert, notably by consuming the more common species and thus advantaging the rarer ones (Clark et al. 2012; Dyer et al. 2010). In addition to the consequences of their herbivory, forest elephants damage the vegetation by trapping saplings as they move and forage, creating and maintaining clearings and a network of trails within the forest that shape vegetal communities (Blake and Inkamba-Nkulu 2004; Hawthorne and Parren 2000; Rosin et al. 2017). These well-established trail networks link large fruit trees and other privileged elements of the landscape that other animal species can benefit from (Blake and Inkamba-Nkulu 2004). Highly frugivorous, forest elephants consume more fruit species than any other large vertebrate (Beirne et al. 2020) and are the only ones to disperse over long distances many large or robustly encapsulated seeds (Guimarães et al. 2008; Beaune et al. 2013) with average placement distances of 7.4 km per day (Mills et al. 2018) and an annual home range of 195 km² (Poulsen et al. 2021). The forest elephant also contributes to nutrient recycling through its faeces (Doughty et al. 2015).

However, the indirect effects of the forest elephant on other vertebrate species remain poorly understood compared to

the savannah elephant, whose role in bird diversity (Ogada et al. 2008), in the abundance of small vertebrates such as lizards (Pringle 2008), in the selection of habitats by other herbivores (Valeix et al. 2011) and as a competitor (Valeix et al. 2008) or facilitator of herbivore species (Makhabu et al. 2006) has been the subject of several studies. We therefore have limited knowledge of forest elephants and their role in the ecosystem, and studies on savannah elephants provide information that is difficult to transpose to forest elephants, given their differences in terms of morphology, ecology and social structure (Blake and Hedges 2004; Eggert et al. 2002; Grubb et al. 2000; Roca et al. 2001). To the best of our knowledge, there have been no studies examining the role of the forest elephant in the frequency and diversity of mammalian communities.

In this context, we aimed to investigate the impact of forest elephants on the diversity and occurrence frequencies of terrestrial mammals within the lowland forest of Conkouati-Douli National Park, Republic of the Congo. This research also aims to contribute to a better understanding of the mammal communities in Conkouati-Douli National Park, shedding light on the ecological factors influencing the spatial distribution of terrestrial mammal species. Specifically, we sought to address two primary questions: (1) how does forest elephant frequentation influence mammal diversity and community composition, and (2) what other ecological factors, beyond elephant frequentation, are involved in the distribution of terrestrial mammals in this ecosystem? We hypothesised that sites with intermediate levels of forest elephant frequentation would support greater mammal diversity due to the enhanced habitat heterogeneity and variety of microhabitats created by elephant-induced alterations in vegetation structure and composition (Maicher et al. 2020; Kamga et al. 2022). Furthermore, we expected that sites with high levels of elephant frequentation might exhibit reduced biodiversity because of habitat degradation or loss of resources for certain species. Finally, we anticipated that the composition of mammal communities would vary spatially based on the intensity of local elephant frequentation and other ecological factors, reflecting differences in ecological niche and resource availability generated by these disturbances.

## 2 | Materials and Methods

### 2.1 | Study Area

We carried out our study inside the Conkouati-Douli National Park, in the Republic of Congo (Figure 1). The park covers 3680 km² of land and 4275 km² of sea, each with its own integrally protected area. To the north, it is bordered by Gabon's Mayumba National Park, with which it includes a block of 6000 km² of forest habitat. Along the coast and in its eastern part, the park includes an eco-development area and a buffer area. In 2023, approximately 7800 people resided within these two areas, in 31 villages located along two roads connecting the park to the city of Pointe-Noire. The coast is primarily inhabited by fishermen, while in the eastern part of the park, main activities are timber harvesting for construction and charcoal production, as well as artisanal mining of gold and cassiterite, and cassava production (Noé 2023). Despite regular mobile patrols and checks at strategic points such as the two
<figure><img src="image_2.png" /><figcaption>**FIGURE 1** | Location of the study area within the Conkouati-Douli National Park, in Southwestern Republic of Congo, and locations of the camera traps within the study area.</figcaption></figure>

access roads and along the main river, the park is subject to a number of human pressures. Poaching for the international ivory trade, trafficking of wild animals and bushmeat hunting for non-local consumption occur in the park, including the integrally protected terrestrial area. In addition, the park suffers from illegal mining, unauthorised logging of timber for urban markets and unregulated industrial fishing. Human-elephant conflicts are also recurrent in the eco-development and buffer areas.

Our study area was located within the integrally protected ter-
restrial area of the park, more precisely in the partially flood-
prone forest situated to the northeast of the Conkouati lagoon
(Figure 1). With a surface area of ca. 38 km², it overlapped the
Ngongo river delta (3°52'36" S. 11°21'54" E) and encompassed
a network of watercourses. As is typically the case in the
Conkouati-Douli National Park (Des Clics Nomades 2025), cli-
mate was characterised by a dry season from May to September,
and a rainy season from October to April with peaks of rainfall
at the beginning and end of the season. However, while the study
period exhibited temperatures similar to those of the previous
decade (on average 23.5°C during the dry season, and 26.0°C
during the rainy season), it was exceptionally dry, with rainfall
totalling 405 mm in 2022 and 553 mm in 2023 vs. 1320–2824 mm
between 2012 and 2021.

2.2 | Data Collection

A total of 38 camera-traps (Browning SPEC OPS, BTC 8E HP4
mode) were deployed between May 5, 2022 and August 9, 2023
over the study area, at a rate of one camera per km² (Figure 1).
The protocol was based on that recommended by the Leibniz
Institute for Zoo and Wildlife Research ‘Studying terrestrial
mammals in tropical rainforests’ (Abrams et al. 2018). Each
camera was installed on a tree trunk, at a height included be-
tween 45 and 218 cm above the ground in order to record the
passage of as many terrestrial mammals as possible, while min-
imising the risk of the camera being drowned during flooding.
It should be stressed that at this height, no strictly arboreal spe-
cies could be observed. Each camera was operating 24 h a day
using infrared LEDs. The cameras were set up in photographic
mode, and at six locations an additional camera was positioned
5–7 m in front of the main camera in video mode. Photographs
were taken in sequences of 5, with a theoretical time interval
of 0.6 s that actually turned out to be between 0.6 and 2 s. The
cameras in video mode were set to record videos of 20 s or more
if the motion sensor detected movement again after 10 to 15 s
of recording. The cameras operated for a minimum of 5 months
and 11 days, and a maximum of 1 year, 3 months and 5 days, de-
pending on location. Batteries and SD cards were changed every
2 to 3 months.
In order to quantify the local density of undergrowth, we took four photographs of a 1.5m high ×1m wide orange tarpaulin, held at ground level and positioned successively at the four cardinal points 10m from the camera trap. Similarly, in order to quantify canopy cover, five photographs of the canopy were taken per site, one in the immediate vicinity of the camera, and four at the north-east, south-east, south-west and north-west corners of a 20m square centred on the camera (Figure 2). In this case, the camera was held vertically at ca. 1.80m above the ground.

Trunks of 10 to 50cm in diameter and those over 50cm in diameter were counted within a 50-m radius of each camera trap (i.e., over an area of 0.79ha). In addition, we noted the presence of swamps within the same radius, as well as signs of poaching (cartridges, waste, etc.) when such signs were discovered during a visit to the site.

## 2.3 | Analysis of Photographs and Videos

Photographs and additional videos taken by the camera traps were manually grouped into sequences using Timelapse software (Doe 2022). Species identification was carried out using the criteria given by Kingdon (2003) for mammals and Serle and Morel (1993) for birds. Animals that could not be identified to species level were assigned to the most precise higher taxon possible, and we excluded higher taxa that could have matched already identified species. Photo series and videos in which several species appeared simultaneously were treated as separate sequences. In order to minimise pseudo-replications, two successive series involving the same species in the same location were only considered as independent events when separated by a time interval of at least 1 h (Sollmann 2018). This threshold is consistent with our own observations. Indeed, individuals or groups of certain species such as the forest elephant or the mandrill (Mandrillus sphinx) were sometimes observed staying in front of the camera for more than 45 min and even 1 h and 15 min in one case concerning the forest

<figure><img src="image_3.png" /><figcaption>**FIGURE 2** | Sketch map of a camera-trap site. The camera-trap is located in P1. P1, P2, P3, P4 and P5 are the places where canopy photographs were taken, whereas P6, P7, P8 and P9 are the places where the orange tarpaulin was placed to quantify the undergrowth density.</figcaption></figure>

elephant. For each sequence of photos and videos, we also noted whether or not the site was flooded.

## 2.4 | Preliminary Calculations

Mammal biodiversity was quantified using three variables: species richness (theoretically the number of species, to which we actually added the number of broader taxa that did not include any identified species), the Shannon index and the Evenness index. The Shannon index was calculated as

$$H' = - \sum p_i \log_2 p_i$$

where $p_i$ was the proportion of individuals belonging to species or higher taxon i. Proportions required for computing the Shannon index were obtained by summing the numbers of individuals recorded for the various independent events of the same species or higher taxon. The Evenness index was calculated as

$$E = \frac{H'}{\log_2 S}$$

where $H'$ was the Shannon index and $S$ was the species richness.

Species richness, Shannon index and Evenness index were calculated for the study area as a whole, as well as for each of the 38 sites. In the latter case, however, the forest elephant was ignored, as this species was intended to act as a predictor of the rest of the biodiversity in our statistical analyses. To this end, the daily flow of forest elephants was calculated for each site as the total number of sighted forest elephants divided by the number of days during which camera traps were active at that site. In addition, undergrowth density and canopy cover were quantified for each site, based on the photographs taken there. To this end, we used ImageJ software (Schneider et al. 2012) to evaluate the percentage of the orange tarpaulin or sky that was hidden by vegetation in the photographs, and then calculated the average of one and the other per site. Finally, we calculated the proportion of time during which each site was flooded while camera traps were active.

Clues of poaching were only found by chance on rare occasions, and likely at unrepresentative sites, as the network of watercourses and elephant trails allows fairly easy access to any part of the study area. We therefore gave up considering a variable distinguishing sites according to hunting pressure.

## 2.5 | Statistical Analyses

As a preliminary analysis, we examined the correlations between the seven variables envisaged as predictors, i.e., (1) daily flow of elephants, (2) undergrowth density, (3) canopy cover, (4) density of trunks 10–50cm in diameter, (5) density of trunks over 50cm, (6) presence of swamps within a 50 m radius and (7) proportion of time the site was flooded (Table 1). The two trunk densities appeared more correlated over the 38 sites ($r = +0.53$) than any other pair of variables ($|r|$ included between 0.01 and 0.43). Accordingly, we decided to sum them
<table><caption>**TABLE 1** | Pairwise Pearson's correlation between the seven variables initially envisaged as predictors, plus an eighth, the total number of trunks > 10 cm (i.e., the sum of the number of trunks 10–50 cm in diameter and that of trunks over 50 cm).</caption><thead><tr><th></th><th>Nb trunks 10–50 cm</th><th>Nb trunks > 50 cm</th><th>Elephant flow</th><th>Canopy cover</th><th>Undergr. density</th><th>Flooding time</th><th>Swamp presence</th><th>Nb trunks > 10 cm</th></tr></thead><tbody><tr><td>Nb trunks 10–50 cm</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Nb trunks > 50 cm</td><td>0.53***</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Elephant flow</td><td>-0.16</td><td>-0.19</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Canopy cover</td><td>0.25</td><td>0.17</td><td>-0.05</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Undergrowth density</td><td>-0.43**</td><td>-0.22</td><td>0.03</td><td>-0.30*</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Flooding time</td><td>0.28*</td><td>0.11</td><td>-0.17</td><td>-0.30*</td><td>0.22</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Swamp presence</td><td>0.03</td><td>-0.07</td><td>-0.27</td><td>-0.07</td><td>0.03</td><td>0.22</td><td>—</td><td>—</td></tr><tr><td>Nb trunks > 10 cm</td><td>0.98***</td><td>0.68***</td><td>-0.18</td><td>0.25</td><td>-0.42**</td><td>0.26</td><td>0.01</td><td>—</td></tr></tbody></table>

and thus consider only the density of trunks over 10 cm among predictors.

We then examined the effects of the six retained variables on the values obtained for species richness, Shannon index and Evenness index using a set of linear models. The most complete model envisaged was

$$Y \sim E + E^2 + U + U^2 + C + C^2 + D + D^2 + F + F^2 + S + T$$

where Y was either species richness, Shannon index or Evenness index (elephants excluded), E the elephant flow, U the undergrowth density, C the canopy cover, D the density of trunks over 10 cm, F the proportion of time flooded, S a binary variable indicating whether swamps were present and T the time (in days) during which cameras were operating. Numerical variables other than T were introduced in both their linear and quadratic forms because we assumed that biodiversity could reach its maximum when some of these variables took intermediate values. As alternatives to the complete model, we considered all the possible submodels including camera operating time-T, and in which any quadratic form was only present if its linear counterpart was present. Models were compared using Akaike Information Criterion corrected for small samples (AICc). The model exhibiting the lowest AICc was retained. However, since the distribution of its residuals did not fit a normal law and suggested heteroscedasticity, we re-estimated the model's p-values by random permutation of the values of Y (total: 5000 permutations), and checked that all were lower than 0.05.

Afterwards, we examined how the distribution of the main mammal species across the 38 sites depended on the six retained environmental variables. To that aim, we considered the species

(other than the forest elephant) that were observed at least ten times and in at least five sites. We built for these species a matrix detailing their 'observation rate' per site (i.e., their number of independent observations divided by camera operating time T). In addition to this 'species matrix', we built a 'variable matrix' giving the value of each of the six environmental variables per site. We then performed a Canonical Correspondence Analysis (CCA; Ter Braak 1986) in which the species matrix was constrained by the variable matrix. Effect of each variable on the species matrix was compared to 0, computing (with 5000 random permutations) the anova-like permutation test recommended by Legendre et al. (2011).

As a complement to CCA, we examined the effects of the six retained environmental variables on the distribution of each species across the 38 sites. This was carried out using a set of linear models and following the same procedure as that described above for species richness and Shannon index. In this case, the most complete model envisaged was

$$\log_e(R + 0.001) \sim E + E^2 + U + U^2 + C + C^2 + D + D^2 + F + F^2 + S$$

where R was the considered species' observation rate, expressed in number of independent observations per day. The smallest non-zero value of R was ca. 0.002, hence the constant 0.001 added to R in the logarithm. As previously, we focused for each species on the model exhibiting the lowest AICc, and reestimated its p-values by random permutation (total: 5000 permutations). If the model included quadratic terms, we retained it when these terms were found to be significant at $\alpha$=0.05 and its linear terms at $\alpha$=0.10. If the model included only linear terms, we retained it when at least one of its terms was significant at $\alpha$=0.05 and the others at $\alpha$=0.10. Otherwise, we considered the

<footnote>*p*<0.1.</footnote>

<footnote>**P**<0.01.</footnote>

<footnote>***P**<0.001.</footnote>
model exhibiting the second lowest AICc value, re-estimated its
p-values and so on.

All the statistical analyses were performed using *R* software,
and more specifically the *MuMln* package (Barton 2023)
for model selection by AICc, *lmPerm* package (Wheeler and
Torchiano 2016) for re-estimating the *P*-values of linear mod-
els', and *vegan* package (Oksanen et al. 2024) for the Canonical
Correspondence Analysis. The plots were created using the gg-
plot2 package (Wickham 2016).

3 | Results

A total of 95,610 snapshots/videos were obtained for an average
of 404.4 recording days per site (range: 162-456 days; median:
451 days). After examination, 5459 independent events were dis-
tinguished, of which 74 (1.36%) could not be satisfactorily as-
signed to any taxon. Among the remaining 5385 events, 5019
concerned mammals, of which 4854 (96.71%) were identified
to the species level and 165 (3.29%) were assigned to a higher
taxonomic level. A total of 31 mammalian species (Table 2)
were identified (mean ± SD of the number of identified species
per location: 11.50 ± 4.53; range: 3-21). It is worth noting that
for several of them, more than a third of independent events
involve a group of several individuals: this was the case for
the forest elephant (1-10 individuals per independent event;
mean ± SD = 2.1 ± 1.4), red river hog (*Potamochoerus porcus*) (1-
71; mean ± SD = 9.1 ± 11.0), blue duiker (*Philantomba monticola*)
(1-3; mean ± SD = 1.4 ± 0.5), red-capped mangabey (*Cercocebus
torquatus*) (1-23; mean ± SD = 2.6 ± 3.5), moustached monkey
(*Cercopithecus cebus*) (1-8; mean ± SD = 1.8 ± 1.0), crowned
monkey (*Cercopithecus pogonias*) (1-3; mean ± SD = 1.8 ± 1.0),
mandrill (1-306; mean ± SD = 10.2 ± 28.0), western lowland
gorilla (*Gorilla gorilla gorilla*) (1-14; mean ± SD = 2.6 ± 2.1)
and chimpanzee (*Pan troglodytes troglodytes*) (1-25;
mean ± SD = 3.5 ± 3.5). In addition, we also observed polyspe-
cific groups involving the crowned monkey, moustached mon-
key, red-capped mangabey, putty-nosed monkey (*Cercopithecus
nictitans*) and mandrill. Taking all locations together, the
Shannon-Wiener index was 3.29 for mammals (forest elephant
included).

**3.1 | Variations Between Sites in Biodiversity Indices**

Species richness of mammals (forest elephant excluded) ranged
from 3 to 22 species between sites (mean ± SD = 11.42 ± 4.84),
reaching its highest value at a site never subject to flooding
and its lowest value at the most frequently flooded site (96%
of time). Besides the number of days the cameras were active,
all the linear models concerning species richness with ΔAICc
≤ 2.00 included the variables forest elephant flow, squared for-
est elephant flow and flooding time (Table 3). The best model
(ΔAICc = 0) was also the most parsimonious and included these
four predictors only. In this model (Table 4), the estimates of the
coefficients for forest elephant flow and squared forest elephant
flow suggest species richness was maximum around 0.44 ele-
phants per day (Figure 3a). The same model confirms a negative
effect of flooding time on species richness (Figure 3b).

Shannon index of mammals (forest elephant excluded) ranged
from 1.00 to 3.16 between sites (mean ± SD = 2.29 ± 0.54), reach-
ing both its highest and lowest values at sites flooded less than
10% of the time. Besides the number of days the cameras were
active, all the linear models concerning the Shannon index
with ΔAICc ≤ 2.00 included the variables forest elephant flow,
squared forest elephant flow, density of trunks over 10 cm in di-
ameter, squared density of trunks over 10 cm in diameter and
flooding time (Table 3). The best model (ΔAICc = 0) was also
the most parsimonious and included these six predictors only.
In this model (Table 4), the estimates of the coefficients for for-
est elephant flow and squared forest elephant flow suggest the
Shannon index was maximum around 0.42 elephants per day
(Figure 3c). The same model indicates a negative effect of flood-
ing time (Figure 3d), as well as a negative effect of the density of
trunks greater than 10 cm in diameter over most of the variable's
range (Figure 3e).

Evenness index of mammals (forest elephant excluded) ranged
from 0.36 to 0.87 between sites (mean ± SD = 0.69 ± 0.13), reach-
ing its highest value at a site flooded more than 95% of the time
and its lowest value at a site flooded less than 5% of the time.
Besides the number of days the cameras were active, all the lin-
ear models concerning the Evenness index with ΔAICc ≤ 2.00
included the variables density of trunks over 10 cm in diameter
and squared density of trunks over 10 cm in diameter (Table 3).
The best model (ΔAICc = 0) was also the most parsimonious and
included these three predictors only. In this model (Table 4), the
estimates of the coefficients for density of trunks over 10 cm in
diameter and squared density of trunks over 10 cm in diameter
suggest the Evenness index was minimum around 15 trunks
within a 50 m radius around the camera (Figure 3f).

**3.2 | Variations Between Sites in the Occurrence Frequency of the Main Mammalian Species**

Twenty-four species of mammals other than the forest elephant
were observed at least 10 times (all sites combined) and in at least
five sites (Table 2). According to the Canonical Correspondence
Analysis (CCA) carried out on these species, the six considered
ecological variables explained 29.7% of the species distribu-
tion across the 38 sites. However, only four of the six variables,
namely swamp presence, flooding time, trunk density and el-
ephant flow, were found to have a significant effect (post hoc
permutation tests, α = 0.05).

Figure 4 shows the CCA results within the plan formed by the first two axes, which together account for 39.7 + 26.5 = 66.2% of the variance explained. In addition, Table 5 points out which of the six variables had a significant effect on the occurrence frequency of each species, according to the linear model retained at the end of the selection process (see Section 2.5).

With the exception of the black-fronted duiker (*Cephalophus ni-
grifrons*), the large duikers (*Cephalophus* spp.) were rare in the
vicinity of swamps (hence their locations on the left in Figure 4).
In contrast, the moustached monkey, putty-nosed monkey, for-
est buffalo (*Syncerus caffer nanus*) and sitatunga (*Tragelaphus
spekii*) were common near swamps, with the sitatunga also often
observed in flood-prone sites, as was the red-capped mangabey.
<table><caption>**TABLE 2** | Species and higher taxa of mammals recorded by camera-traps across all sites, their IUCN status, the number of sites where they have been observed, the number of independent observations, and the mean number of individuals per independent observation.</caption><thead><tr><th>TAXON family/ order</th><th>Scientific name Common name</th><th>Red list status (IUCN)</th><th>Number of sites where the species has been observed (over 38)</th><th>Number of independent observations</th><th>Mean number of individuals per independent observation</th></tr></thead><tbody><tr><td colspan="6"><strong>Mammals</strong></td></tr><tr><td>Elephantidae</td><td>Loxodonta cyclotis Forest elephant</td><td>CR</td><td>33</td><td>1490</td><td>2.12</td></tr><tr><td>Chiroptera</td><td></td><td>—</td><td>15</td><td>111</td><td>1.11</td></tr><tr><td>Suidae</td><td>Potamochoerus porcus Red river hog</td><td>LC</td><td>31</td><td>290</td><td>9.10</td></tr><tr><td>Tragulidae</td><td>Hyemoschus aquaticus Water chevrotain</td><td>LC</td><td>5</td><td>8</td><td>1.00</td></tr><tr><td rowspan="8">Bovidae</td><td>Syncerus caffer nanus Forest buffalo</td><td>NT</td><td>10</td><td>26</td><td>1.35</td></tr><tr><td>Tragelaphus spekii Sitatunga</td><td>LC</td><td>21</td><td>121</td><td>1.02</td></tr><tr><td>Cephalophus dorsalis Bay duiker</td><td>NT</td><td>8</td><td>35</td><td>1.00</td></tr><tr><td>Cephalophus nigrifrons Black-fronted duiker</td><td>LC</td><td>10</td><td>26</td><td>1.04</td></tr><tr><td>Cephalophus ogilbyi Ogilby's duiker</td><td>LC</td><td>16</td><td>300</td><td>1.07</td></tr><tr><td>Cephalophus silvicultor Yellow-backed duiker</td><td>NT</td><td>20</td><td>126</td><td>1.05</td></tr><tr><td>Philantomba monticola Blue duiker</td><td>LC</td><td>24</td><td>444</td><td>1.44</td></tr><tr><td>Nandiniidae</td><td>Nandinia binotata African palm civet</td><td>LC</td><td>1</td><td>1</td><td>1.00</td></tr><tr><td rowspan="2">Felidae</td><td>Caracal aurata Golden cat</td><td>VU</td><td>4</td><td>5</td><td>1.00</td></tr><tr><td>Panthera pardus Leopard</td><td>VU</td><td>15</td><td>25</td><td>1.00</td></tr><tr><td rowspan="2">Viverridae</td><td>Civettictis civetta African civet</td><td>LC</td><td>3</td><td>3</td><td>1.00</td></tr><tr><td>Genetta servalina Genette servaline</td><td>LC</td><td>16</td><td>68</td><td>1.03</td></tr><tr><td rowspan="3">Herpestidae</td><td>Atilax paludinosus Marsh mongoose</td><td>LC</td><td>6</td><td>26</td><td>1.08</td></tr><tr><td>Bdeogale nigripes Black-legged mongoose</td><td>LC</td><td>1</td><td>2</td><td>1.00</td></tr><tr><td>Xenogale naso Long-snouted mongoose</td><td>LC</td><td>5</td><td>11</td><td>1.09</td></tr><tr><td>Manidae</td><td>Phataginus tricuspis Tree pangolin</td><td>EN</td><td>3</td><td>4</td><td>1.00</td></tr></tbody></table>

(Continues)
<table><caption>TABLE 2 | (Continued)</caption><thead><tr><th>TAXON family/ order</th><th>Scientific name Common name</th><th>Red list status (IUCN)</th><th>Number of sites where the species has been observed (over 38)</th><th>Number of independent observations</th><th>Mean number of individuals per independent observation</th></tr></thead><tbody><tr><td>Galagonidae</td><td>Galagoides sp.</td><td>—</td><td>1</td><td>1</td><td>1.00</td></tr><tr><td rowspan="5">Cercopithecidae</td><td>Cercocebus torquatus Red-capped mangabey</td><td>EN</td><td>22</td><td>150</td><td>2.65</td></tr><tr><td>Cercopithecus cephus Moustached monkey</td><td>LC</td><td>21</td><td>94</td><td>1.78</td></tr><tr><td>Cercopithecus nictitans Putty-nosed monkey</td><td>NT</td><td>7</td><td>48</td><td>1.06</td></tr><tr><td>Cercopithecus pogonias Crowned monkey</td><td>VU</td><td>1</td><td>24</td><td>1.83</td></tr><tr><td>Mandrillus sphinx Mandrill</td><td>VU</td><td>29</td><td>254</td><td>10.17</td></tr><tr><td rowspan="2">Hominidae</td><td>Gorilla gorilla gorilla Western lowland gorilla</td><td>CR</td><td>35</td><td>322</td><td>2.61</td></tr><tr><td>Pan troglodytes troglodytes Chimpanzee</td><td>EN</td><td>37</td><td>290</td><td>3.47</td></tr><tr><td>Hystricidae</td><td>Atherurus africanus Brush-tailed porcupine</td><td>LC</td><td>17</td><td>351</td><td>1.13</td></tr><tr><td>Nesomyidae</td><td>Cricetomys sp.</td><td>—</td><td>4</td><td>8</td><td>1.12</td></tr><tr><td>Muridae</td><td>—</td><td>—</td><td>10</td><td>45</td><td>1.00</td></tr><tr><td rowspan="5">Sciuridae</td><td>Epixerus ebii wilsoni Biafran bight palm squirrel</td><td>LC</td><td>14</td><td>88</td><td>1.02</td></tr><tr><td>Funisciurus lemniscatus Ribboned rope squirrel</td><td>LC</td><td>9</td><td>62</td><td>1.06</td></tr><tr><td>Funisciurus pyrropus Fire-footed rope squirrel</td><td>LC</td><td>12</td><td>157</td><td>1.05</td></tr><tr><td>Protoxerus stangeri African giant squirrel</td><td>LC</td><td>1</td><td>3</td><td>1.00</td></tr></tbody></table>

Unlike the mandrill and chimpanzee, the gorilla tended to use sites with a low density of trunks over 10cm in diameter. Furthermore, a number of species tended to favour sites where elephant flow was high or at least non-negligible (Table 5). This was the case for the red river hog, at least three bovids (forest buffalo, yellow-backed duiker *Cephalophus silvicultor*, blue duiker), the brush-tailed porcupine (*Atherurus africanus*), three monkeys (red-capped mangabey, putty-nosed monkey and mandrill), the chimpanzee and gorilla, as well as some carnivores (leopard *Panthera pardus*, servaline genet *Genetta servalina* and marsh mongoose *Atilax paludinosus*).

4 | Discussion

In this study, we examined the effect of the forest elephant on the biodiversity and occurrence frequency of terrestrial

mammals, using 38 camera traps set up in a lowland forest in Conkouati-Douli National Park, Republic of Congo. Our study highlights the significant role played by the forest elephant in structuring local mammal biodiversity. This finding is in line with the scientific literature describing the forest elephant as an 'ecosystem engineer' (Poulsen et al. 2018; Cardoso et al. 2020; Kamga et al. 2022; Blake and Maisels 2023).

Species richness and Shannon index values were maximum for a forest elephant flow included between 0.4 and 0.5 elephants per day. This finding, which is in agreement with our first hypothesis, suggests that, by their presence, movements and foraging behaviour, forest elephants can create suitable microhabitats for numerous ground-dwelling mammals through the rainforest when they are neither too few nor too abundant. Indeed, forest elephants, through consistent use of patches, can influence vegetation structure, composition and dynamics: they can inhibit or
<table><caption>TABLE 3 | AICc and ΔAICc values for linear models of species richness, Shannon index and Evenness index with ΔAICc ≤ 2.</caption><thead><tr><th>Dependent variable</th><th>Model</th><th>Number of parameters (k)</th><th>AIC</th><th>ΔAIC</th><th>Weight (W<sub>i</sub>)</th></tr></thead><tbody><tr><td rowspan="4">Species richness</td><td>E + E<sup>2</sup> + F + T</td><td>4</td><td>218.67</td><td>0.00</td><td>0.09</td></tr><tr><td>E + E<sup>2</sup> + F + D + T</td><td>5</td><td>219.29</td><td>0.62</td><td>0.06</td></tr><tr><td>E + E<sup>2</sup> + F + S + T</td><td>5</td><td>219.96</td><td>1.29</td><td>0.04</td></tr><tr><td>E + E<sup>2</sup> + F + U + T</td><td>5</td><td>220.44</td><td>1.77</td><td>0.04</td></tr><tr><td rowspan="3">Shannon index</td><td>E + E<sup>2</sup> + F + D + D<sup>2</sup> + T</td><td>6</td><td>36.88</td><td>0.00</td><td>0.17</td></tr><tr><td>E + E<sup>2</sup> + F + D + D<sup>2</sup> + C + T</td><td>7</td><td>37.91</td><td>1.03</td><td>0.10</td></tr><tr><td>E + E<sup>2</sup> + F + D + D<sup>2</sup> + S + T</td><td>7</td><td>38.86</td><td>1.97</td><td>0.06</td></tr><tr><td rowspan="4">Evenness index</td><td>D + D<sup>2</sup> + T</td><td>3</td><td>-52.54</td><td>0.00</td><td>0.14</td></tr><tr><td>D + D<sup>2</sup> + U + T</td><td>4</td><td>-51.70</td><td>0.83</td><td>0.09</td></tr><tr><td>D + D<sup>2</sup> + F + T</td><td>4</td><td>-51.62</td><td>0.91</td><td>0.09</td></tr><tr><td>D + D<sup>2</sup> + C + T</td><td>4</td><td>-50.94</td><td>1.59</td><td>0.06</td></tr></tbody></table>

Abbreviations: C, canopy cover; D, number of trunks over 10 cm in diameter within a 50-m radius around the camera trap; D², squared value of the previous variable; E, daily forest elephant flow; E², squared value of the previous variable; F, proportion of time sites were flooded; S, presence of swamps within a 50-m radius around the camera trap; T, number of days the cameras were active; U, undergrowth density. Best models are in bold.

<table><caption>TABLE 4 | Best models for species richness, Shannon index and Evenness index, with estimate and standard error of coefficients, corresponding Student's t test, and p-value re-estimated by random permutation.</caption><thead><tr><th>Dependent variable</th><th>Independent variable</th><th>Estimate</th><th>SE</th><th>t</th><th>p</th></tr></thead><tbody><tr><td rowspan="5">Species richness</td><td>(Intercept)</td><td>0.875</td><td>3.484</td><td>0.251</td><td>0.8032</td></tr><tr><td>Daily elephant flow</td><td>19.192</td><td>7.502</td><td>2.558</td><td>0.0154*</td></tr><tr><td>Squared elephant flow</td><td>-21.799</td><td>8.122</td><td>-2.684</td><td>0.0096**</td></tr><tr><td>Flooding time (proportion)</td><td>-8.437</td><td>3.297</td><td>-2.559</td><td>0.0110*</td></tr><tr><td>Recording time (days)</td><td>0.024</td><td>0.009</td><td>2.845</td><td>0.0126*</td></tr><tr><td rowspan="6">Shannon index</td><td>(Intercept)</td><td>1.9533</td><td>0.463</td><td>4.217</td><td>0.0002***</td></tr><tr><td>Daily elephant flow</td><td>2.1096</td><td>0.684</td><td>3.085</td><td>0.0032**</td></tr><tr><td>Squared elephant flow</td><td>-2.4981</td><td>0.766</td><td>-3.262</td><td>&lt;0.0001***</td></tr><tr><td>Flooding time (proportion)</td><td>-1.2419</td><td>0.322</td><td>-3.861</td><td>&lt;0.0001***</td></tr><tr><td>Nb trunks &gt; 10 cm</td><td>-0.1296</td><td>0.047</td><td>-2.772</td><td>0.0018**</td></tr><tr><td>Squared Nb trunks &gt; 10 cm</td><td>0.0034</td><td>0.002</td><td>2.260</td><td>0.0212*</td></tr><tr><td rowspan="3">Evenness index</td><td>(Intercept)</td><td>0.8151</td><td>0.144</td><td>5.659</td><td>&lt;0.0001***</td></tr><tr><td>Nb trunks &gt; 10 cm</td><td>-0.0449</td><td>0.013</td><td>-3.406</td><td>0.0026**</td></tr><tr><td>Squared Nb trunks &gt; 10 cm</td><td>0.0014</td><td>0.000</td><td>3.395</td><td>0.0026**</td></tr></tbody></table>

favour plant regeneration and shape vegetal communities by dispersing seeds, selectively browsing certain plant species, damaging trees and trampling saplings, reducing vegetation density and promoting the establishment of pioneer species (Hawthorne and Parren 2000; Blake et al. 2009; Campos-Arceiz and Blake 2011; Omeja et al. 2014; Poulsen et al. 2017). In addition, forest elephants can create and maintain clearings and trails

within the forest that connect key elements of the landscape like *bais* (water meadows) and fruit trees (Blake and Inkamban-Kulu 2004). All of these modifications of the habitat induced by forest elephants are likely to affect the distribution and composition of other species by promoting habitat heterogeneity and creating distinct ecological niches (Ripple et al. 2015; Kamga et al. 2022). Recent studies have shown that habitat alterations

<footnote>*P < 0.05.</footnote>

<footnote>**P < 0.01.</footnote>

<footnote>***P < 0.001.</footnote>
<figure><img src="image_4.png" /><figcaption>**FIGURE 3** | Illustration of the effects of forest elephant flow (a) and flooding time (b) in the best model for species richness, the effects of forest ele-
phant flow (c), flooding time (d) and density of trunks over 10 cm in diameter (e) in the best model for the Shannon index, and the effects of density of
trunks over 10 cm in diameter (f) in the best model for the Evenness index (Table 4). The points correspond to the values of species richness, Shannon
index and Evenness index (forest elephant always excluded) after subtracting the effects of the independent variables other than the one on the x-axis.</figcaption></figure>

created by forest elephants influence the species richness and
the composition of lepidopterans (Maicher et al. 2020), and that
disturbances to vegetation structure caused by forest elephants
promote the diversity of bird assemblages through increased
habitat heterogeneity (Kamga et al. 2022).

As expected, we found that the biodiversity of terrestrial mammals declined for high values of forest elephant flow. Lower values of the Shannon index and species richness observed at sites highly frequented by elephants could be explained by resource exploitation competition (Head et al. 2012) and excessive habitat
<figure><img src="image_5.png" /><figcaption>**FIGURE 4** | Results of the Canonical Correspondence Analysis performed on the mammal species observed at least 10 times (all sites combined) and in five different sites, and relating their distribution to the six ecological variables considered. Ecological variables with significant effects (α=0.05) are represented by solid lines, whereas non-significant variables are represented by dashed lines.</figcaption></figure>

degradation. Alternatively, it might be hypothesised that these low values are due to increased poaching along the busiest ele- phant trails because such trails facilitate human movement in the understorey. However, there is no evidence of a correlation between elephant flow and poaching pressure in the present study: the rare clues of poaching that we found near camera traps were not located in sites with high elephant flow (median: 0.09 elephants per day for sites with clues, against 0.11 elephants per day for the other sites).

The effect of elephant flow on the Shannon index, primarily through its influence on species richness, underscores the role of the keystone species in shaping community diversity. This find- ing supports the idea that elephants act as ecosystem engineers, with their movement patterns contributing to species diversity in a dynamic way. On the other hand, the observation that trunk density affects the Shannon index by primarily influencing the Evenness index emphasises the role of habitat structure in regu- lating species distribution and abundance. Trunk density can be linked to various ecological factors such as resource partition- ing, competition and habitat complexity. A high trunk density

may provide more niches or microhabitats, enabling a greater balance among species and reducing the dominance of a few. Conversely, low trunk density might limit resource availability or habitat complexity, potentially skewing community composi- tion toward dominant species and reducing evenness.

In our study, various mammal species appeared to be favoured by intermediary levels of frequentation by forest elephants, their occurrence frequency being maximum for a flow of forest elephants included between 0.4 and 0.6 elephants per day. These species, such as the brush-tailed porcupine, red- capped mangabey and putty-nosed monkey, might benefit from the opening and disruption of vegetation to find refuges to protect themselves from predators and to forage. Duikers, which are generally frugivorous (Lumpkin and Kranz 1984; Huffman 2011), may benefit from the trails and the fallen fruits not consumed by elephants. Indeed, forest elephants have been observed dropping fruits from trees using several methods, including shaking the base of the trunk with their head or using objects (Maisels et al. 2002). We can then assume that the remains of fallen fruits not eaten by forest elephants
<table><caption>TABLE 5 | Variables present in the model retained for each of the 24 species considered in the CCA.</caption><thead><tr><th></th><th>Swamp presence</th><th>Flooding time</th><th>Nb of trunks</th><th>Elephant flow</th><th>Canopy cover</th><th>Undergr. density</th></tr></thead><tbody><tr><td>Chiroptera</td><td></td><td></td><td>—</td><td>0.51</td><td></td><td></td></tr><tr><td>Red river hog</td><td></td><td>(+)</td><td></td><td>0.51</td><td></td><td></td></tr><tr><td>Forest buffalo</td><td>+</td><td></td><td></td><td>+</td><td>—</td><td></td></tr><tr><td>Sitatunga</td><td>+</td><td>0.52</td><td></td><td></td><td></td><td></td></tr><tr><td>Black-fronted duiker</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Bay duiker</td><td>—</td><td></td><td></td><td></td><td></td><td>—</td></tr><tr><td>Ogilby's duiker</td><td>—</td><td></td><td></td><td>(+)</td><td></td><td>(—)</td></tr><tr><td>Yellow-backed duiker</td><td>—</td><td></td><td></td><td>0.52</td><td>+</td><td></td></tr><tr><td>Blue duiker</td><td></td><td></td><td></td><td>0.47</td><td>(+)</td><td></td></tr><tr><td>Leopard</td><td></td><td></td><td></td><td>0.57</td><td></td><td></td></tr><tr><td>Servaline genet</td><td></td><td></td><td></td><td>0.48</td><td></td><td></td></tr><tr><td>Marsh mongoose</td><td></td><td></td><td></td><td>+</td><td></td><td></td></tr><tr><td>Long-snouted mongoose</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Red-capped mangabey</td><td></td><td>0.46</td><td></td><td>0.43</td><td></td><td>—</td></tr><tr><td>Moustached monkey</td><td>+</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Putty-nosed monkey</td><td>+</td><td></td><td></td><td>0.48</td><td></td><td></td></tr><tr><td>Mandrill</td><td></td><td></td><td>17</td><td>+</td><td>(+)</td><td></td></tr><tr><td>Gorilla</td><td></td><td>(—)</td><td>—</td><td>0.57</td><td></td><td></td></tr><tr><td>Chimpanzee</td><td></td><td></td><td>15</td><td>+</td><td></td><td></td></tr><tr><td>Brush-tailed porcupine</td><td></td><td></td><td></td><td>0.45</td><td>+</td><td>+</td></tr><tr><td>Muridae</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Biafran palm squirrel</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Ribboned rope squirrel</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Fire-footed rope squirrel</td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

Note: When a variable was retained in its quadratic form, the value that maximises the species' occurrence frequency is given. When a variable was retained in its linear form, only the sign of its effect on occurrence frequency is given, with (+) and (—) indicating effects only significant at α = 0.10. No model was better than the null model for black-fronted duiker, long-snouted mongoose, muridae and the three squirrel species.

may attract duikers and other frugivorous animals. Gautier-Hion and Gautier (1994) observed Ogilby's duikers eating any fallen fruits under trees where monkeys were foraging, and Kingdon (2003) reports that the blue duiker is able to follow the tracks of primates and birds to feed on the remains of fallen fruits. The preference of gorillas for areas with an intermediate flow of forest elephants is probably linked to the action of elephants in modifying the habitat. By opening up the canopy, forest elephants create clearings that encourage the growth of young plants and shrubs, providing an essential food source for gorillas (Estes 1991). In addition, elephants facilitate movement by creating paths through the dense vegetation. Also, lowland gorillas use *bais* to supplement their diet (Sienne et al. 2014), and they could benefit from the forest elephant trails to get there. Furthermore, the association of the red river hog with the forest elephant is not surprising, since the latter regularly feeds on seeds and larvae found in forest elephant dung, and shows a preference for clearings

where it finds a greater diversity of food resources (Meijaard et al. 2011; Reyna et al. 2016), possibly thanks to the forest elephant (Melletti et al. 2017). Moreover, we can assume the association of the two predators—the leopard and servaline genet—with an intermediary flow of forest elephants is due to a hunting success enhanced by the environment opening (Loarie et al. 2013; Tambling et al. 2013). As forest elephant trails can potentially be used by other species, notably red river hogs and bovids, this could also be a place where the leopard encounters more prey and therefore a privileged location for hunting.

Four species were found to have a positive linear relationship with the daily flow of forest elephants: the chimpanzee, mandrill, forest buffalo and marsh mongoose. As for duikers, we can assume the chimpanzee, which is highly frugivorous (Tweheyo et al. 2004), could benefit from the trails to feed on fruits. Like the gorilla, the mandrill may benefit
from the opening up of the habitat to feed on herbaceous stratum. During the dry season, when food is scarcer, mandrills often exploit the growth of herbaceous strata after disturbances to the vegetation, such as logging or crop changes (Kingdon 2003). In the same way, they may exploit the growth of young grasses after the passage of elephants, which open up the environment and allow this stratum to grow. The association of the forest buffalo with important daily flows of forest elephants can be explained by its preference for clearings and more open habitats, which facilitate social interactions between conspecifics (Melletti et al. 2007), but also by its use of *bais*, most of the time linked by trails created by forest elephants (Blake and Inkamba-Nkulu 2004; Gessner et al. 2014).

As expected, we found that the occurrence frequency of a number of forest mammals also varied with other factors than elephant flow. To begin with, while species richness and Shannon index decreased with increasing flooding time, two species tended to often use flood-prone sites: the sitatunga and red-capped mangabey – a monkey known to use this type of habitat predominantly (Estes 1991). Similarly, several species were frequently observed in the vicinity of swamps. This was once again the case for the sitatunga, but also the forest buffalo, moustached monkey and putty-nosed monkey. This result is not surprising for the first three species, all known for their intensive use of marshy habitats (Gautier-Hion et al. 1981; Estes 1991; Blake 2002b; Abernethy and Maisels 2020), but rather unexpected for the putty-nosed monkey, for which swampy areas are considered a secondary habitat where the species is uncommon (Cronin et al. 2020). Unlike the four previous species, three duikers – the bay duiker, Ogilby's duiker and yellow-backed duiker – were found avoiding marshy sites, while two others – the black-fronted duiker and blue duiker – did not appear to do so.

Occurrence frequency of some mammalian species was also found to vary with the density of trunks over 10 cm in diameter. Gorillas, in particular, preferred sites with a low trunk density, which is undoubtedly linked to their dietary habits and exclusively terrestrial mode of locomotion (Estes 1991). In contrast, occurrence frequency of chimpanzees and mandrills was maximum when there were 15 to 17 trunks in a 50-m radius around the camera. These semi-terrestrial primates use both the ground and trees to travel and forage, and find refuges in trees (Hoshino 1985; Lahm 1986; McLennan 2013). Accordingly, a fairly high density of tree trunks probably offers them the most favourable habitat.

For some species, we also found that occurrence frequency varied with undergrowth density and/or canopy cover. Bay duikers were mainly observed where undergrowth was sparse, a result consistent with the reported preference of the species for primary forest (Kingdon 2003; Huffman 2011). Though less documented, a similar preference has been suggested by McCollum et al. (2018) for the yellow-backed duiker, which we did observe in closed-canopy sites. Our results for the brush-tailed porcupine were more unexpected: whereas the species is reported to live primarily near water (Jori et al. 1998; Kaleme et al. 2007; Hoffmann and Cox 2016), we did not particularly observe it near swamps, but in sites that tended to have a dense undergrowth despite a closed canopy. Finally, and consistent with its known preference for clearings (Blake 2002b; Melletti et al. 2007; Leslie

Jr. 2011), the forest buffalo was frequently observed in sites with low canopy cover.

While our study focused on the impact of forest elephants on the biodiversity and composition of other mammal species, one key factor not considered is the presence of poachers. Poaching activities are known to influence the distribution and behaviour of both elephants and hunted mammals (Blom et al. 2005; Laurance et al. 2006; Breuer et al. 2016). Although poachers have access to the study area through elephant trails and waterways, it was difficult to quantify this influence across sites given the homogeneous nature of access. In future studies, incorporating poaching intensity or presence as an explanatory variable could help to better isolate its effects on the ecological dynamics observed in this study. This would allow for a more comprehensive understanding of the factors influencing species distribution and biodiversity in these ecosystems.

To conclude, this study highlights the crucial role of forest elephants as ecosystem architects, shaping biodiversity and the composition of terrestrial mammal communities. Their complex and multifaceted influence reveals a delicate balance: an intermediate flow of elephants appears to promote maximum mammal richness and diversity, while both excessively high and low flows can lead to reduced diversity and ecological imbalances. Through their ability to open the canopy, create clearings and disperse seeds, forest elephants play a vital role in habitat structuring and the creation of ecological niches for many species. However, the cascading effects of these disturbances on animal communities, particularly the interactions between predators, prey and food resources, warrant further research to better understand these complex dynamics. In the future, it would be pertinent to explore more in-depth the interspecific interactions between forest elephants and other animal species, particularly mammals, to better understand how these relationships influence community structuring and ecological dynamics in forest habitats. Additionally, examining how climate change and anthropogenic pressures, such as deforestation and poaching, alter these ecological interactions and influence elephant populations and their effects on biodiversity is important. A better understanding of these processes could provide valuable insights for the management and conservation of Afrotropical forest ecosystems, balancing the preservation of forest elephants with the need to maintain biodiversity and ecosystem processes such as forest habitat resilience.

## Acknowledgements

We acknowledge HELP Congo's staff, and particularly Andrea Sotto-Mayor, Anselme Taty, Thierry Nimi, Alphonse Engusu, Grace Mbemba, Jorda Nkounkou, Matthieu Fromet and Léo Gonzalez, for collecting the data. We are grateful to Beauval Nature for funding this study, and especially Eric Bairrao-Ruivo for his support and implication throughout the project. We gratefully thank the UGP (Park management unit) of the Conkouati-Douli National Park and the Ministry of Forest Economy of the Republic of Congo for allowing this study to be carried out. Finally, our thanks are due to two anonymous reviewers for their constructive remarks on an earlier draft of the manuscript.

## Conflicts of Interest

The authors declare no conflicts of interest.
# Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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