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Open AccessArticle

Native and Invasive Small Mammals in Urban Habitats along the Commercial Axis Connecting Benin and Niger, West Africa

1
Faculté des Sciences et Techniques, Université Abdou Moumouni, Niamey BP 10662, Niger
2
Ecole Polytechnique d’Abomey-Calavi, Laboratoire de Recherche en Biologie Appliquée, Université d’Abomey-Calavi, Cotonou 01 BP 2009, Benin
3
Direction Générale de la Protection des Végétaux, Niamey BP 323, Niger
4
Centre de Biologie pour la Gestion des Populations, UMR IRD-INRA-Cirad-Montpellier SupAgro-MUSE, Campus International de Baillarguet, 755 avenue du Campus Agropolis, CS 30016, 34988 Montferrier-sur-Lez, France
5
Centre Régional Agrhymet, Rue de l’Université, Niamey BP 11011, Niger
6
IRD, Aix Marseille Université, LPED, 13331 Marseille Cedex 3, France
*
Authors to whom correspondence should be addressed.
Diversity 2019, 11(12), 238; https://doi.org/10.3390/d11120238
Received: 18 October 2019 / Revised: 26 November 2019 / Accepted: 4 December 2019 / Published: 10 December 2019
(This article belongs to the Special Issue Biological Invasions 2020 Horizon)

Abstract

Based on compiled small mammal trapping data collected over 12 years from Benin and Niger (3701 individual records from 66 sampling sites), located in mainland Africa, we here describe the small mammal community assemblage in urban habitats along the commercial axis connecting the two countries, from the seaport of Cotonou to the Sahelian hinterland, with a particular focus on invasive species. In doing so, we document extant species distributions, which highlight the risks of continuing the range expansion of three synanthropic invasive rodent species, namely black rats (Rattus rattus), brown rats (R. norvegicus), and house mice (Mus musculus). Using various diversity estimates and community ecology approaches, we detect a latitudinal gradient of species richness that significantly decreased Northward. We show that shrews (Crocidura) represent a very important component of micro-mammal fauna in West African towns and villages, especially at lower latitudes. We also demonstrate that invasive and native synanthropic rodents do not distribute randomly in West Africa, which suggests that invasive species dynamics and history differ markedly, and that they involve gradual, as well as human-mediated, long distance dispersal. Patterns of segregation are also observed between native Mastomys natalensis and invasive rats R. rattus and R. norvegicus, suggesting potential native-to-invasive species turn over. Consequences of such processes, especially in terms of public health, are discussed.
Keywords: synanthropic rodents; biological invasion; community ecology; Rattus; Mus; West Africa synanthropic rodents; biological invasion; community ecology; Rattus; Mus; West Africa

1. Introduction

In addition to the various effects of global changes such as climate modification [1], shift in land-use [2,3], and urbanization [4,5,6,7], West Africa faces great impacts from international trade, such as biological invasions [8,9]. Biological invasions occur from the rapid development of major commercial hubs (like international seaports and airports), as well as the densifying of road and river transportation networks [10,11].
Arrival, proliferation, and spatial expansion of invasive species can deeply alter an ecosystems functioning [12] and the community structure of species [13,14,15,16]. In the case of successful bioinvasions, communities tend to be simplified and homogenized [17,18,19] and exotic species usually become numerically dominant [20]. The bioinvasion of rodent species are usually intimately associated with urbanization, human trade, and transportation [21,22,23,24]. Rattus spp. and Mus musculus are widely introduced rodent species [25], and when established, they may cause very severe damage to crops and food stocks [26]. They may also deeply impact public health through the worldwide dissemination of pathogens [27,28,29,30] and greatly threaten native biodiversity through predation, competition, and extinction of local species, as well as the modification of host/parasite interactions [31,32,33,34,35,36,37].
Rats and mice were first introduced in West Africa in coastal cities, probably from the pre-colonial period to the present day, and they disseminated within lands, following human movements and goods trade [23,24,38,39,40]. These invasive rodents expanded rapidly inland over the last decades, with their dispersal being tightly associated with fluvial and road networks, transport improvement, as well as growing urbanization [22,23,24,38,40,41,42,43,44,45]. As such, history was important in shaping current distributions of invasive rodent species in West Africa.
Niger is a land-locked country that operates its import and export products from the closest seaports, in particular Cotonou seaport in Benin [46], through which nearly 80% of its international trade transits [47]. As a consequence, the Benin–Niger corridor is one of the main transnational trade routes for Niger, especially for cereals and other food products [48,49]. In such a context, recent surveys have highlighted the probable role of road and fluvial traffic in the invasion and progression of black rats in Southwest Niger [24,41].
Based on extensive trapping data obtained in recent years (see methods), we here present a survey of small mammal faunas in a series of cities and villages from Benin and Niger. In doing so, we aim to describe the distribution and the spread of commensal small mammals along the coast–hinterland commercial axis connecting Benin and Niger, with a particular focus on three invasive rodent species, namely black rats (Rattus rattus), brown rats (Rattus norvegicus), and house mice (Mus musculus). Using these baseline data, we discuss the implications of bioinvasions on small mammal communities and alert sanitary and agricultural authorities about the potentially ongoing and rapid geographic expansion of these commensal invasive species. As such, this study contributes to Aichi Biodiversity Target #9 on invasive alien species.

2. Material and Methods

2.1. Sampling

Between 2005 and 2017, terrestrial small mammal (i.e., including rodents and shrews) sampling was conducted in commensal habitats in Benin and Niger during the course of different research programs. Sampling sites were distributed along a South–North gradient that included several coastal localities of Southern Benin (i.e., Porto-Novo, Ganvié, Togbin, Ouidah, and Cotonou, including its international seaport), a few inland cities of central and Northern Benin (i.e., Bohicon, Dassa-Zoumé, Parakou, and Kandi), as well as South-Western Niger, along the Niger River (i.e., Boumba, Say, and Hamma Dendi), and the main tarred roads connecting Niamey, the capital city of Niger, to the surrounding countries (i.e., Makalondi at the Burkinabè border; Dosso and Dogondoucthi along the national road towards Eastern Niger and Northern Nigeria; Gaya at the Benin/Niger border; and Tillabery on the road to Mali) (Figure 1). Our dataset thus includes 18 localities from Southern Guinean to Northern Sahelian localities. Localities refer to prospected villages and cities. In terms of surface area, they ranged from small villages of less than one square kilometre (i.e., Hamma Dendi, and Ganvié) to large urban agglomerations extended over several tens of square kilometres (i.e., Cotonou, Niamey). Sites corresponded to sampling points within these localities. We aimed to sample each site within an area of the same order of magnitude. Thus, villages and small cities were here considered as single sites, when in the two large cities, sites referred to different urban neighborhoods/districts. In total, 66 sampling sites, with a minimum of 10-captured small mammal individuals (36 sites in Benin, 30 in Niger), were considered in the present study. In particular, the two largest cities, i.e., Cotonou and Niamey, were both intensively and extensively sampled (28 and 22 different sites in Cotonou and Niamey, respectively).
Two to four traps were set inside each sampled room in a building (houses, storehouses, shops, offices, and markets) for three–five consecutive nights. Commensal habitats were irregular and complex, and given that traps were set indoor, ethical guidelines imposed that trapping procedures strictly respect the everyday life of people living there. As a consequence, it was not feasible to arrange lines or grids when trapping inside buildings. Investigated households were targeted, following the acceptance of inhabitants as well spatial coverage, with buildings separated by 10–200 m, according to site spatial organization. Baits consisted of peanut butter, usually supplemented with fish or ‘soumbala’ (local spice made from the Néré tree, Parkia biglobosa). Minimal overall sampling effort, of approximately 27,142 trap-nights, was deployed (Table 1). In order to capture small (e.g., Mus spp., etc.), medium-sized (e.g., Mastomys spp.), to moderately large rodent species (e.g., Rattus spp., etc.), as well as shrews (Crocidura spp.), two models of traps were most often used in combination and in similar proportions: Sherman folding box traps (8 × 9 × 23 cm; H.B. Sherman Traps, Inc., Tallahassee, FL, USA) and locally made wire-mesh live traps (10 × 10 × 25 cm), with the exception of a subset of the most ancient sampling sessions (23 sites) in Benin, where only wire-mesh live traps (Firobind model, 8 × 8 × 25 cm, and locally made model, 10 × 10 × 25 cm), baited with a mixture of wheat flour and caned sardine or smoked fish, were used.
Small mammals were captured alive and immediately brought to the lab where they were processed the same day or the day after. They were euthanatized with diethyl–ether or chloroform for the most ancient sampling sessions, and sacrificed by cervical dislocation, as recommended by Mills et al. [50] for the most recent sessions. Sex and standard body measurements (weight, head + body, tail, ear, and hindfoot lengths) were recorded. The reproductive status was also noted for both sexes. Tissue samples were collected for further analyses, such as taxonomic barcoding, population genetics, and pathogen screening, etc. All samples were deposited in CBGP Small Mammal Collection, Montpellier, France (https://doi.org/10.15454/WWNUPO) and in LARBA sample collection, Cotonou, Benin.

2.2. Species-Specific Identifications

Due to the frequent coexistence of cryptic species in West Africa [51], we were cautious to provide unambiguous specific assignments, in particular for most if not all rats, mice, and multi-mammate rats, which were of primary interest for the purposes of the present study. As such, our species-specific identifications relied on morphological grounds when no ambiguity existed (e.g., adult rats and house mice). However, very young individuals or individuals belonging to sibling species complexes were identified through laboratory methods [41,52]. To do so, we performed karyotyping (e.g., Mastomys spp. [34]), nuclear microsatellite genotyping (e.g., Rattus rattus, R. norvegicus and Mastomys natalensis), or cytochrome b mitochondrial gene sequencing (e.g., Rattus spp., Mastomys spp., Praomys spp., Arvicanthis spp., and Crocidura spp. [24,34,53]; our unpublished data).

2.3. Ethics Statements

All trapping sessions were conducted under explicit agreements with local, traditional (e.g., family and household heads, shops, and firm owners), as well as administrative (i.e., Cotonou City Hall services, Crop Protection Division in Niger, urban district chiefs) authorities. None of the rodent species captured in the present study had a protected status (see IUCN and CITES lists). All animals were treated in a humane manner, in accordance with the guidelines from the American Society of Mammalogists [54]. Fieldwork in Benin was conducted under the research agreement between the Republic of Benin and the French National Research Institute for Sustainable Development (IRD), which was signed on 30th September 2010 (available upon request). Those in Niger were authorized by the scientific partnership agreement (number 301027/00) between IRD and the Republic of Niger.

2.4. Data Analyses

A smoothed sample-based rarefaction curve, rescaled by individuals, was computed with the program Estimate S 9.1.0 [55] to determine whether most common taxa were been detected in the dataset [56]. Correlations, e.g., between a species relative abundance and latitude, were tested with the nonparametric Spearman rank correlation coefficient (rS). P-values were two-tailed.

2.4.1. Distribution of Native and Invasive Small Mammals

Geographical maps were produced to illustrate species-specific distributions. We analyzed spatial variations of the distribution of the different species. To do so, we approximated, for each site, estimates of relative abundances of the different species in the samples as the proportions of the total number of individuals trapped within a site belonging to a given species, hereafter designed as p(species). For each species, we explored spatial variation in p(species) along the South–North axis by testing for a correlation with latitude. The dataset, being a compilation of trapping results from different research programs, was not appropriate to investigate temporal variation. This study nevertheless provides baseline data so that this could be an area for future research.
The association between sampling sites and trapped species was then described through a factorial correspondence analysis (FCA), implemented in R, using the Ade4 package [57,58] as well as cartography graphics designed in the R-adegraphics package [59]. The multivariate analyses presented here focused on the most frequently trapped taxa (i.e., detected in ≥25% of the trapping sessions), namely: M. natalensis, R. rattus, R. norvegicus, and Crocidura spp.

2.4.2. Species Diversity

The variation of species diversity, both within sites (alpha-diversity) and between sites (beta-diversity), was investigated. For each trapping site, small mammal species richness was computed as an estimate of alpha-diversity using the rarefaction method implemented in the software BiodivR [60], using a subsample of 10-trapped individuals drawn at random. The Simpson (1-D) index of diversity and Shannon H entropy were estimated using PAST v.3.25 software [61]. We investigated the spatial variation of species richness and the Simpson and Shannon diversity indices by testing for a correlation with latitude. The comparison of species richness between groups of sampling sites was done using a Mann–Whitney non-parametric test.
Beta-diversity was a measure of change in the diversity of species between sites: High beta-diversity indicated a low level of similarity, while low beta-diversity showed a high level of similarity. For each pair of sites, the similarity of trapped small mammal communities was estimated using two indices: The incidence-based Jaccard and the abundance-based Morisita–Horn indices [62], both implemented in the software EstimateS 9.1.0 [55]. The Jaccard index was based on presence–absence data, and thus estimated the similarity of species lists. The Morisita–Horn index was based on a species relative abundance, gave a strong weight to the most abundant species, and was not sensitive to variations in species richness or sample size [63].
We explored the respective influence of the spatial distribution of sites and of the presence–absence of invasive species on the similarity of communities. The influence of each explanatory variable (pairwise spatial distance between sites and invasion status) was first tested separately and then tested in combined analyses. The matrix of pairwise similarity of invasion status was based on the presence–absence of invasive species; it was coded as 1 when two sites shared either the detection of any invasive species (i.e., both sites were invaded) or none of them (i.e., in both sites, only native species were detected), and as 0 when two sites differed in the detection of invasive species (i.e., one site with at least one of the three invasive species and the other site only with native species). The variation explained by two variables may be correlated and therefore partly redundant. In order to determine the proportion of community similarity, explained by each explanatory variable when controlling for the effect of the other variable, we used the method of variance partitioning [64,65,66], thus allowing us to calculate the part of the variance explained by each variable, the variation shared between variables, and the residual variance. In doing so, we inferred the relative and independent statistical effects of each variable in structuring community similarity. Statistical significance was investigated using partial Mantel tests (10,000 permutations) under the program FSTAT 2.9.3.2 [67].

2.4.3. Co-Occurrence Patterns

Patterns of co-occurrence of commensal small mammal species were compared at the regional level as follows. Data were organized as matrices of the occurrence of captured events: Each row referred to the number of trapped individuals of a given species, while each column corresponded to a trapping site. We compared the observed and expected patterns under the null hypothesis of random assembly [68], using the standardized C-score (SCS) [69] as a quantitative index of co-occurrence, with a significant negative and positive SCS, indicating aggregation and segregation, respectively. To assess the statistical significance, the observed SCSs were compared to the values obtained from 10,000 iterations using null models with random matrices constrained for row and column totals to match the original matrix (‘fixed-fixed’ algorithm, model 1). We checked that the outcome of the ‘fixed-equiprobable’ algorithm (model 2), another recommended model [68], was identical. We estimated pairwise co-occurrence scores using Pairs 1.1 [70] and applied the confidence limit criterion [71] to determine whether a particular pair of species was statistically aggregated, segregated, or randomly associated. Results of this co-occurrence analysis are presented for four taxa that were frequently trapped (in ≥25% of the trapping sites for each taxon), namely M. natalensis, R. rattus, Crocidura spp., and R. norvegicus. We also investigated possible species association among all sampled sites through the test of a correlation between pairs of p(species) for the different species pairs.

3. Results

3.1. Trapping Results

The sample-based rarefaction curve, rescaled by individuals, was based on small mammal trapping data in the 66 urban sites and approached a plateau, suggesting that the most common taxa have been sampled in the whole investigated area (Figure 2). However, sampling was not intensive enough in some sites to assess how the rarefaction curve differed between sites. Trapping resulted in a total of 3701 captured individuals (Table 1), including three invasive species: Rattus rattus (1765 individuals), Rattus norvegicus (272), and Mus musculus (73), as well as six native taxa: Mastomys natalensis (912 individuals), Crocidura spp. (610), Arvicanthis spp. (24), Praomys spp. (22), Cricetomys spp. (15), and Mastomys erythroleucus (8). For each of the rarely collected rodent genera, a minimum of two different species have been identified along the commercial axis connecting Benin and Niger: Arvicanthis niloticus from the “C2–C4” clade (sensu [72]) in the Northern sites, and Arvicanthis species from the “ansorgei evolutionary group” in the Southern sites (possibly ANI-2 cytotype of A. rufinus, though we note that A. ansorgei has also been previously detected in sympatry with A. niloticus in Gaya at the border between Niger and Benin) ([72,73], Gauthier and Dobigny, unpublished data); and Praomys daltoni in Niger and P. derooi in Benin ([53,74], Mikula et al., submitted). Cricetomys individuals from Niamey were unambiguously diagnosed as C. gambianus, but molecular data of those from Cotonou was lacking. Most captured shrews of genus Crocidura were made of large size individuals, usually referred to as C. olivieri species complex (clades I and II in Jacquet et al. [75]), along with a few C. viaria (clade III in Jacquet et al. [75]) small individuals in Niamey. To be more cautious, given that the taxonomy of Crocidura warrants a throughout revision, and because some of the Crocidura and of the rarely trapped rodent specimens have only been identified by morphology, we decided to conservatively refer to individuals from these genera as undetermined (i.e., ‘spp.’). Hereafter, we confidently made the hypothesis that the results were not strongly biased by this taxonomic lumping.
A median number of 34 small mammal individuals were captured per site (range: 12 to 341). Mastomys natalensis was detected in 86.4% of the 66 sites, followed by R. rattus (71.2% of sites), Crocidura spp. (69.7%), R. norvegicus (33.3%), Arvicanthis spp. (10.6%), Praomys spp. (6.1%), Cricetomys spp. (4.5%), Mus musculus (3.0%), and Mastomys erythroleucus (1.5%).

3.2. Distribution of Native and Invasive Small Mammals

Invasive species were captured in most of the sampled trapping sites (48/66 = 73%) and localities (15/18 = 83%), with the invasive R. rattus being the most frequently captured species of the whole dataset (Table 1).
Factorial correspondence analysis (Figure 3A) revealed two distinct groups of sites that were discriminated along the F1 axis: On the left (negative values), mainly Nigerien sites were characterized by M. natalensis abundance, while sites where R. rattus numerically dominated, mostly from Benin, lie on the right side, i.e., towards positive values. Clear exceptions were the localities of Togbin in Benin where M. natalensis was found to be more abundant than R. rattus and Hamma Dendi in Niger where only R. rattus was captured (Table 1).
We found a rather strong and significant correlation between site coordinates on the F1 axis of the FCA and their respective latitude (rS = −0.588, P < 10−3, n = 66), suggesting a latitudinal variation of the community structure.
The cartography of species distribution (Figure 3B) showed that two species, R. rattus and M. natalensis, were found in most sampling sites in both Benin and Niger. In terms of abundance, M. natalensis dominated in Northern sites (Niger), while R. rattus dominated in Southern sites (Benin) where R. rattus had been detected in every site, except in the Ganhi market of Cotonou, in which R. norvegicus was the most frequently trapped species. Data from Cotonou (South) and Niamey (North), the two localities where extensive and fine-grained data have been collected, suggest that R. rattus populations are distributed as “continuous nappes” in the South, but only in isolated patches in the North.
As to R. norvegicus captures, they were mostly restricted to sites in coastal, Southern Benin; the Northernmost locality where the species have been detected (Dassa-Zoumé in 2017) is 210 km North from the coast. Mus musculus species was unambiguously detected in only two sites: The Autonomous Port of Cotonou and the Great Market of Niamey.
The native species M. natalensis was detected in all Northern and Southern sampled localities, except Hamma Dendi in Niger (where, only R. rattus was found) and Ganvié in Benin (where the rodent community largely consists of R. rattus, and to a lesser extent, R. norvegicus). However, this is in opposition to the R. rattus distribution, where it was found that M. natalensis distributions were continuous in the North sites, particularly in the well-sampled city of Niamey [34], and in patches in Benin, especially in the well-sampled city of Cotonou (our own unpublished data).
Crocidura spp. were more abundant in Southern sites of Benin than in Niger sites; this was particularly noticeable in the cities of Niamey and Cotonou (6.9% and 22.2%, respectively), where large and equivalent trapping efforts were made.
Cricetomys spp. were only captured occasionally in Cotonou and Niamey. However, this may not reflect its real abundance and distribution due to the likely low probabilities of trapping this large animal with the models and sizes of traps employed here.
A statistically significant latitudinal gradient of relative abundances of the different species, expressed as p(species), was detected for four taxa: M. natalensis (rS = 0.538, P < 10−3, n = 66), R. rattus (rS = −0.404, P < 10−3, n = 66), R. norvegicus (rS = −0.648, P < 10−3, n = 66), and Crocidura spp. (rS = −0.429, P < 10−3, n = 66). From South to North, p(M. natalensis) increased, while p(R. rattus), p(R. norvegicus) and p(Crocidura spp.) decreased. For the other taxa that were much less frequently trapped, no significant spatial variation could be detected (all P ≥ 0.09).

3.3. Species Diversity

A non-random spatial community structure was retrieved along a latitudinal (i.e., South–North) gradient. Local species richness of captured commensal small mammals, i.e., including both native and invasive species, was estimated by rarefaction for 10 sampled individuals and ranged between one and four species, with a mean ± SE of 2.17 ± 0.09 species per trapping site (n = 66). Estimated species richness decreased significantly Northward (rS = −0.589, n = 66, P < 10−3), with higher values in most of Beninese sites (mean ± SE = 2.56 ± 0.11, n = 36) than those observed in Nigerien sites (mean ± SE = 1.71 ± 0.10, n = 30; Mann-Whitney test P < 10−3). Notably, this pattern of latitudinal variation was also observed for native species richness (sites from Benin: mean ± SE = 2.00 ± 0.13, n = 12; Niger: 1.58 ± 0.11, n = 23; Mann-Whitney test P = 0.0178; estimates based on 10 sampled individuals of native species). The number of shared species between sites varied from zero to five species. The same pattern of spatial variation was observed for the Simpson (1-D) index of diversity (rS = −0.487, n = 66, P < 10−3) and for the Shannon H entropy (rS = −0.552, n = 66, P < 10−3).
The similarity of commensal small mammal assemblages varied with the invasion status, and to a lesser extent, with spatial distance, showing a distance decay of species similarity (Table 2).
Community similarity differs according to the invasion status: Pairs of sites where only native species were detected showed high a similarity (median value for Morisita–Horn index = 0.978 and for Jaccard index = 0.500, 153 pairs of sites), like the pairs of sites there were both invaded (median value for Morisita–Horn index = 0.813 and for Jaccard index = 0.500, 1128 pairs of sites). On the contrary, pairs of sites that differed in the detection of invasive species (one site with at least one of the three invasive species and the other site only with native species) exhibited low similarity (median value for Morisita–Horn index = 0.082 and for Jaccard index = 0.333, 864 pairs of sites). Taken together, the invasion status and spatial distance variables explained 44.6% of Morisita–Horn similarity and 24.4% of Jaccard similarity. The pure component explained by the invasion status showed the highest contribution (25.7% of Morisita–Horn similarity and 9.4% of Jaccard similarity). Overall, 15.4% of Morisita–Horn and 9.9% of Jaccard similarity variation was shared between the invasion status and spatial variables.

3.4. Co-Occurrence Patterns

The two null models for testing species co-occurrence at the regional level showed a pattern of segregation between native M. natalensis and invasive Rattus spp. This pattern of segregation was significant for R. norvegicus (model 1: P = 0.003, model 2: P = 0.014), while only marginally non-significant for R. rattus for one of the models (model 1: P = 0.14, model 2: P = 0.088). Notably, a significant negative correlation between R. rattus and M. natalensis relative abundance (i.e., p(R. rattus) and p(M. natalensis)) was found (rS = −0.730, P < 10−3). We found significant aggregation patterns between R. norvegicus and R. rattus, and between R. norvegicus and Crocidura spp. (Table 3).

4. Discussion

4.1. Distributions of Native and Invasive Small Mammals

We detected a total of nine taxa, including the three invasive species Rattus rattus, R. norvegicus, and Mus musculus, and at least six native taxa, namely Mastomys natalensis and Crocidura spp. (≥2 species), and at a much lower frequency, Arvicanthis spp. (2–3 species), Praomys spp. (2 species), Cricetomys spp., and Mastomys erythroleucus. While additional species were expected in the community (e.g., Xerus erythropus was observed outdoor in Niamey; the pygmy mice Mus hausa was captured by hand indoor in Hamma Dendi and Mus baoulei was trapped in peri-urban areas of Cotonou that were not included in the present study), such a set of more than 12 trapped species is typical of commensal West African Sudano–Sahelian small mammal communities [51]. As a comparison, in Senegal, a total of 17 taxa of commensal small mammals have been recorded over three decades, including the same three invasive species [23]. In Nigeria, 19 small mammal species, including invasive R. rattus, were detected from nine localities across three ecological zones [76]. Similar diversity estimates and species assemblages have already been reported in recent surveys conducted in commensal habitats in other West African countries [77,78,79].
M. natalensis, R. rattus, and Crocidura spp. were the most abundantly trapped species as they have been detected in both Benin and Niger, with, respectively, 86.4%, 71.2%, and 69.7% of the 66 sites sampled. Apart from a few exceptions (Togbin in Benin a peri-urban site that still remains separated from Cotonou city by mangroves, and Hamma Dendi in Niger located along the Niger River away from main road axes), two distinct groups of localities were identified: One group consisting mainly of Nigerien localities characterized by M. natalensis numerical dominance on the one hand, and another group of Beninese localities where R. rattus was more frequently trapped on the other hand.
The most abundant native species, M. natalensis, was detected in both Northern and Southern localities. Similar distribution patterns, including low abundances of most other native species, have been observed elsewhere, thus indicating that M. natalensis is the most frequently captured native commensal small mammal species in various ecological zones in West Africa [34,51,76,80]. Its distribution was found to be locally continuous in the Northern sites, particularly in the well-sampled city of Niamey [34], while in Benin, especially in the well-sampled city of Cotonou, where invasive species dominate, it was rather in patches (see Table 1). Invasive species were found in most of the sampled sites and localities.
The most frequently trapped invasive species was R. rattus. We cannot formally exclude that this could partly result from a non-random choice of trapping sites in several localities (Gaya, Dosso in Niger; Kandi, Parakou, Bohicon, and Dassa-Zoumé in Benin), which were prospected essentially as part of a research program focusing on invasive R. rattus. However, the observed high frequency of R. rattus was not surprising, given the worldwide geographic distribution of this species and the suggestion that it is well adapted to warm and humid climates [19,22]. This species is already widely distributed in West African villages and cities [51], although it is probably still expanding in the area, especially Northward within the Sahelian region, following the road and river traffic [24].
In other parts of the world, several studies have reported that, when established, R. norvegicus is the most common species in urban rodent communities [81,82,83,84]. However, in the present study, R. norvegicus was mostly restricted to coastal cities and one village (i.e., Ganvié) in Southern Benin, less than ca. 200 km from the coast. This could indicate that its Northward expansion is currently in progress, but is still limited, or alternatively, that it has not found favorable environmental conditions to establish and proliferate further North. The last hypothesis is congruent with the fact that R. norvegicus is generally the most abundant at sites with a large proportion of highly vegetated urban coverage and the presence of permanent bodies of water [19]. Indeed this species has long been thought to be restricted to seaports and the islands of Africa, and not to be adapted to dry Sahelian cities (but see Rosevear [85], p. 278). Nevertheless, we propose that further attention should be paid to this species, since many localities with shallows between Dassa–Zoumé and Niger exist and were not investigated, while they may well be favorable for the brown rat. We highly recommend the monitoring of sites in these areas and along the river of Niger, including Niamey, to detect R. norvegicus at the earlier stages of possible introductions. Indeed, R. norvegicus clearly has the potential to establish inland in West Africa, as already observed in Bamako, Mali [86], the Office du Niger area in Mali (our own unpublished observations), Podor, Senegal [23], and Kano, Nigeria [53]; it may then dominate the other small mammal species, massively affect human infrastructures and food resources, and act as a reservoir of many zoonotic pathogens of public health importance [87,88].
Another major invasive species, the house mouse, Mus musculus, was only detected in Cotonou and Niamey, where, despite extensive samples, it was restricted to industrial–commercial sites, i.e., the Autonomous Port of Cotonou and the Great Market of Niamey. We argue that these trading sites may act as ‘invasion hubs’ and as bridgeheads towards neighboring localities (see Dalecky et al. [23] for a proposed role of markets in the range expansion of M. musculus in Senegal). This suggests that house mice may have been transported to Niamey directly from Cotonou harbor where many trucks embark crops and other goods that are exported by the road directly to the Niger capital city. This would represent a >1000 km long-distance inland jump for this species that has not been observed elsewhere in Niger to date ([89]; this study). Population genetic studies should test this hypothesis.
Crocidura spp. was significantly more abundant in Southern localities of Benin than in Niger localities. This was particularly noticeable in Niamey and Cotonou, which were investigated through very large trapping efforts. A similar abundance of shrews in urban assemblages of small mammals has already been reported in West Africa [76]. Note that some Crocidura species are known to be synanthropic, tightly associated with human dwellings, and very dependent on human resources [75,90]. We cannot exclude that parts of the Crocidura species found in Niamey could have had a human-assisted origin. Shrews are very numerous in the outdoor and in the commensal environments from Southern Benin, notably Cotonou (22.2% of all captures), while they were found at much lower frequencies in Niamey (6.9%). This suggests that they may prefer subequatorial rather than Sahelian climates. Cities may provide buffered conditions, allowing them to colonize drier regions such as Niger. As for invasive rodent species, their dissemination could also be associated with human trade and transportation. This would reinforce the hypothesis that the possible mechanism of dispersal in some large Crocidura species could be associated with human-mediated transportation [75,91], but it deserves to be fully demonstrated in West Africa.

4.2. Species Diversity and Co-Occurrence Patterns

The estimated local species richness in commensal habitats is typically low, as already observed for small mammals in other studies [23,92]. We found that the species richness is higher in Southern localities than in Northern ones, which warrants considering all taxa, and when the analysis is restricted, to native taxa. This suggests that the observed decrease of diversity Northward cannot be explained merely by the impact of invasive species, but that other historical, ecological, and biogeographical processes may be acting as well. A non-random spatial community structure was retrieved along a latitudinal gradient. Spatial variation of relative abundances of the different species was detected for the four most frequently trapped taxa; from South to North, it increased for M. natalensis, while it decreased for R. rattus, Crocidura spp., and R. norvegicus. Finally, we found that the similarity of commensal small mammal assemblages varies strongly with the status of invasion, and to a lesser extent, with the spatial distance between sites. Differences in species composition and relative abundance within small mammal assemblage in relation to geographical and environmental factors have been previously reported in several other studies [93,94,95,96,97,98,99].
A non-random pattern of species associations was observed along the Benin–Niger commercial axis. As such, segregation between native M. natalensis and the two invasive rat species (i.e., R. rattus and R. norvegicus) was detected. We also found a significant negative correlation between R. rattus and M. natalensis relative abundances. The results of co-occurrence analyses, including M. musculus, were not significant (P ≥ 0.11, data not shown), probably due to the very low number of sites (2/66) where this species was detected, thus providing too low of a statistical power. Segregation between invasive R. rattus and M. musculus, and native M. natalensis, has been previously detected on commensal rodents at a local scale in specific districts of Niamey city [34]. Here, we also found aggregation patterns within species assemblages at the regional level, particularly between R. norvegicus and R. rattus, on the one hand, and between R. norvegicus and Crocidura spp. on the other. At that stage, the determinants underlying the latter patterns remain unexplained; fine-scale resolution eco-ethological studies are required to address such an issue.
Our results suggest that the dissemination of invasive rodent species (rats and mice) is most probably ongoing in this part of West Africa. We provide evidence that the introduction and proliferation of these invasive organisms has a negative impact on the native commensal M. natalensis, as this was already suggested at the scale of Niamey [34] or in other parts of Africa (e.g., Senegal) [38]. If confirmed, such a native-to-invasive turn over in rodent species would have important consequences in terms of public health. For instance, M. natalensis is the main reservoir of the Lassa virus, which is responsible for annual epidemics of Lassa hemorragic fevers in West Africa (review in Monath [100]), including Benin [101]. To date, the virus has never been identified in Rattus spp., thus likely precluding widespread circulation of the zoonotic agents in Southern Benin cities where R. rattus have largely replaced M. natalensis. Similar shifts in epidemiological dynamics probably concern many other rodent-borne zoonotic pathogens, which may interact differently with various small mammal species. Such consequences could be more severe given the growing and ongoing urbanization [102], which is known to lead to many challenges for global health and the epidemiology of infectious diseases [103,104].

5. Conclusions

At that stage, we found that native M. natalensis is still a major component of the synanthropic small mammal assemblages along the commercial axis connecting Benin and Niger, although the species may be locally replaced by invasive taxa where the latter settle. Our results also show that shrews may represent a very important component of micro-mammal fauna in West African towns and villages, especially at lower latitudes. As a conclusion, invasive and native synanthropic rodents do not distribute randomly in West Africa. Our results also suggest that invasive rodent species (rats and mice) dynamics and history differ markedly, and their dissemination is most probably ongoing in this part of West Africa.
In such a context, we firmly recommend that further studies and monitoring programs should be implemented to infer the sources, routes, and tempo of colonization of the three invasive rodent species, as well as to document their impact in terms of biodiversity and sanitary risks in urban ecosystems.

Author Contributions

Conceptualization, G.D., K.H., G.H., and A.D.; methodology, A.D., G.D., K.H. and G.H.; formal analysis, A.D., K.H., and O.F.-G.; investigation, G.H., S.B., M.G., K.H., G.D., P.G., H.-J.D., J.E., S.G., E.A.; writing original draft preparation, K.H., A.D. and G.D.; writing, review and editing, G.D., A.D. and K.H.

Funding

This research was supported by the French National Research Institute for Sustainable Development (IRD, France), Abomey-Calavi University (UAC, Benin) and Abdou Moumouni University (UAM, Niger). KH has been supported by visiting researcher grants from AMU and IRD during the writing of this paper.

Acknowledgments

This work could not be accomplished without the joint collaborative effort of the members of the “Observatoire des petits Mammifères Indicateurs des Changements Environnementaux” (ObsMiCE) and the “Jeune Equipe Associée à l’IRD—Invasions Biologiques en Afrique de l’Ouest” (JEAI-IBAO). We thank all colleagues and collaborators for their respective contribution in collecting and/or analysing data included in this study. We would also like to thank Caroline Tatard for her help for the molecular identification of many of the specimens collected. Thanks to Souley Yero K. from Regional Center Agrhymet for drawing the map of the investigated localities. Tasnime Adamjy is thanked for her reading and English editing. Four reviewers are thanked for their helpful comments. We are particularly grateful to all householders and local authorities from Benin and Niger for allowing us to set traps inside their homes and workplaces.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Map summarizing the different sampled localities and their distribution along the corridor axis, from South (Cotonou, Benin) to North (Tillabery, Niger). Squares represent the two largest cities (Cotonou and Niamey), which include several sampling sites (see text for details). The other localities, considered as one sampling site, are represented by triangles. BOH = Bohicon; BOU = Boumba; COT = Cotonou; DAS = Dassa-Zoumé; DOG = Dogondoutchi; DOS = Dosso; GAN = Ganvié; GAY = Gaya; HAM = Hamma-Dendi; KAN = Kandi; MAK = Makalondi; NIA = Niamey; OUI = Ouidah; PAR = Parakou; POR = Porto-Novo; SAY= Say; TIL = Tillabery; and TOG = Togbin.
Figure 1. Map summarizing the different sampled localities and their distribution along the corridor axis, from South (Cotonou, Benin) to North (Tillabery, Niger). Squares represent the two largest cities (Cotonou and Niamey), which include several sampling sites (see text for details). The other localities, considered as one sampling site, are represented by triangles. BOH = Bohicon; BOU = Boumba; COT = Cotonou; DAS = Dassa-Zoumé; DOG = Dogondoutchi; DOS = Dosso; GAN = Ganvié; GAY = Gaya; HAM = Hamma-Dendi; KAN = Kandi; MAK = Makalondi; NIA = Niamey; OUI = Ouidah; PAR = Parakou; POR = Porto-Novo; SAY= Say; TIL = Tillabery; and TOG = Togbin.
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Figure 2. Sample-based rarefaction curve rescaled by individuals representing the number of commensal small mammal taxa for a given number of captured individuals over 66 urban sites from Benin and Niger. Grey lines refer to the 95% confidence intervals.
Figure 2. Sample-based rarefaction curve rescaled by individuals representing the number of commensal small mammal taxa for a given number of captured individuals over 66 urban sites from Benin and Niger. Grey lines refer to the 95% confidence intervals.
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Figure 3. Factorial analysis results. A (left panel): Factorial map of the two first axes of the factorial correspondence analysis on the most frequently trapped taxa at 66 sites of urban habitats along the commercial axis connecting Benin and Niger, revealing two distinct groups of sites, on the left (negative values), from Northern Nigerien sites characterized by M. natalensis abundance to Southern Beninese sites on the right (towards positive values), where R. rattus dominates numerically. Lines connect sites from the same country. B (right panel): Cartography of species and their abundance distribution on the two first axes of the factorial correspondence analysis.
Figure 3. Factorial analysis results. A (left panel): Factorial map of the two first axes of the factorial correspondence analysis on the most frequently trapped taxa at 66 sites of urban habitats along the commercial axis connecting Benin and Niger, revealing two distinct groups of sites, on the left (negative values), from Northern Nigerien sites characterized by M. natalensis abundance to Southern Beninese sites on the right (towards positive values), where R. rattus dominates numerically. Lines connect sites from the same country. B (right panel): Cartography of species and their abundance distribution on the two first axes of the factorial correspondence analysis.
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Table 1. Small mammal species sampled in 66 sites of urban habitats along the commercial axis connecting Benin and Niger, from South to North. PAC refers to the Autonomous international Port of Cotonou. For each site, an estimate of the trapping effort is provided. For each taxon, the number of trapped individuals is provided. Invasive species: R. ra.: Rattus rattus, R. no.: Rattus norvegicus, M. mus.: Mus musculus. Native species: M. nat.: Mastomys natalensis, Cro.: Crocidura spp., Arvi.: Arvicanthis spp., Prao.: Praomys spp., Cri.: Cricetomys spp., and M. ery.: Mastomys erythroleucus. Diversity indices: S(10), number of species in a subsample of 10 individuals; 1-D, Simpson index; and H, Shannon entropy.
Table 1. Small mammal species sampled in 66 sites of urban habitats along the commercial axis connecting Benin and Niger, from South to North. PAC refers to the Autonomous international Port of Cotonou. For each site, an estimate of the trapping effort is provided. For each taxon, the number of trapped individuals is provided. Invasive species: R. ra.: Rattus rattus, R. no.: Rattus norvegicus, M. mus.: Mus musculus. Native species: M. nat.: Mastomys natalensis, Cro.: Crocidura spp., Arvi.: Arvicanthis spp., Prao.: Praomys spp., Cri.: Cricetomys spp., and M. ery.: Mastomys erythroleucus. Diversity indices: S(10), number of species in a subsample of 10 individuals; 1-D, Simpson index; and H, Shannon entropy.
LocalitiesSitesLatitude (°N)Longitude (°E)Sampling Date(s)Trapping Effort (Trap-Nights)Invasive SpeciesNative SpeciesTotal CapturesDiversity Indices
R. ra.R. no.M. mu.M. nat.Cro.Arvi.Prao.Cri.M. ery.S(10)1-DH
CotonouPAC6.3482.4312006, 2014, 201584038991238700002393.350.671.23
CotonouWlacodji6.3512.4422006270970330000223.710.691.27
CotonouMarché Ganhi6.3552.4372005600180070000251.990.400.59
TogbinTogbin6.3552.30520172626003540000452.450.370.68
CotonouJacques6.3582.4572006451300230000182.750.440.78
CotonouEnagnon6.3622.45320061801790330000323.360.621.14
CotonouSt-Jean 6.3632.4182010, 2016, 201717911280017930141202643.280.631.19
CotonouAbokicodji6.3632.4422009, 20102403710320000432.210.250.55
CotonouBokossi 6.3652.4382009, 20101509100070000262.980.661.09
CotonouTokpa 6.3652.43420061501870020000272.590.480.81
CotonouDédokpo6.3692.43920062101680110000262.760.520.91
OuidahOuidah6.3722.076201572011560393640002003.220.601.13
CotonouKpankpan6.3732.43920062701801230000243.230.621.11
CotonouMarché Tokpa6.3742.4302006602270110000312.600.440.80
CotonouAgla6.3752.3632010, 2016, 20171728168270578206103413.540.661.28
CotonouChankpamè6.3782.48620062701800460000282.800.520.89
CotonouZogbohouè6.3792.38920064304600110000481.420.080.20
CotonouAdogléta6.3812.4382005, 200660520240000133.920.711.31
CotonouSuru-Léré6.3822.46220062403500210000381.730.150.33
CotonouDjidjè6.3842.4342006 210680040000182.980.641.06
CotonouKowégbo6.3872.46920062102810000000291.340.070.15
CotonouAhouansori6.3882.42320052102200110000241.830.160.34
CotonouLadji 6.3892.4332010, 2016, 20171744182160510101003052.590.530.91
CotonouAvotrou6.3892.476200536040001310000542.140.390.64
CotonouMinonchou6.3912.45720061502300140000282.210.300.56
CotonouAyimlonfidé6.3922.56720172102820000000301.560.120.24
CotonouGankpodo6.3932.45620061502800540000372.550.400.72
CotonouFifadji6.3952.3982006, 201621069190160000952.510.430.78
CotonouVossa Kpodji6.3972.40020062504850210000562.150.260.54
CotonouGodomey6.4132.31220064801400110000162.250.230.46
GanviéGanvié6.4692.39720172303660010000432.050.280.51
Porto NovoPorto Novo6.4972.629201558012940306700002302.890.581.02
BohiconBohicon7.1922.076201727644002180000732.690.540.91
Dassa-ZouméDassa-Zoumé7.7852.19920171691520950000313.410.651.18
Parakou Parakou 9.3762.63020171903400140000391.970.230.45
KandiKandi11.1352.93620172803800100000391.260.050.12
GayaGaya11.8773.45120113043200602000402.290.340.61
BoumbaBoumba12.4092.84020112018004103000522.320.350.64
MakalondiMakalondi12.8361.68720123340001500000151.000.000.00
Hamma DendiHamma Dendi13.03272.378520113263000000000301.000.000.00
DossoDosso13.0423.19820115433100500000361.830.240.40
SaySay13.0962.36020113380001807000251.990.400.59
NiameyGnalga13.4792.11420104000002730000301.720.180.33
NiameyPont Kennedy13.4852.10220104480004040000441.660.170.30
NiameyAbattoirs13.4902.12320104787700000000771.00.000.00
NiameyKaradje 1–213.4942.0972009, 201112900005080000581.800.240.40
NiameyGamkalé Q13.4942.12520104520002300000231.000.000.00
NiameyKirkissoye13.4952.110201072524001304020433.070.591.05
NiameyCGA13.5022.11220103261900100000201.500.100.20
NiameyLamorde13.5072.07720104180003610000371.270.050.12
NiameyCYA13.5122.09920105004006100000651.500.120.23
NiameyPetit Marché13.5142.110201137413002400000371.990.460.65
NiameyWadata13.5182.14420104970001160000172.000.460.65
NiameyGrand Marché13.5192.11520113057061030000712.040.250.49
NiameyEntrepôt CYA13.5202.08120096611900003000221.860.240.40
NiameyRoute Filingué13.5212.152201137000015150000302.000.500.69
NiameyYantala Bas13.5272.08220104490002710000281.360.070.15
NiameyYantala Haut13.5342.08220104840002300000231.000.000.00
NiameyBoukoki13.5372.11320104490004720000491.370.080.17
NiameyBanifandou13.5442.13620103700003380000411.920.310.49
NiameyDaressalam13.5462.09620105310004010000411.240.050.11
NiameyKoubia13.5522.05420103780002620000281.600.130.26
NiameyKoirategui13.5892.10920102660001020000121.980.280.45
NiameyTchangare13.5892.10120102280001600000161.000.000.00
DogondoutchiDogondoutchi13.6444.034201219316002200100392.250.510.78
TillaberyTillabery14.2181.4552011619000601008152.670.550.88
Total27,14217652727391261024221583701
Table 2. Variance partitioning into “spatial” and “invasion status” components of the small mammal community similarity in urban habitats along the commercial axis connecting Benin and Niger. The similarity was estimated using the Morisit–Horn and the Jaccard indices. [I + S] = total variation explained by the invasion status and spatial variables together, [I] = variation explained by invasion status variable, [S] = variation explained by spatial variable, [I|S] = pure invasion status variation, [S|I] = pure spatial variation, [I∩S] = spatial structuring in the similarity data that is shared by the invasion status data, and 1 − [I + S] = unexplained variation.
Table 2. Variance partitioning into “spatial” and “invasion status” components of the small mammal community similarity in urban habitats along the commercial axis connecting Benin and Niger. The similarity was estimated using the Morisit–Horn and the Jaccard indices. [I + S] = total variation explained by the invasion status and spatial variables together, [I] = variation explained by invasion status variable, [S] = variation explained by spatial variable, [I|S] = pure invasion status variation, [S|I] = pure spatial variation, [I∩S] = spatial structuring in the similarity data that is shared by the invasion status data, and 1 − [I + S] = unexplained variation.
VariationMorisita-Horn Similarity IndexJaccard Similarity Index
Variance (%)PVariance (%)P
[I + S]44.6<0.000124.4<0.0001
[I]41.1<0.000119.2<0.0001
[S]18.9<0.000115.0<0.0001
[I|S]25.7<0.00019.4<0.0001
[S|I]3.5<0.00015.2<0.0001
[I∩S]15.4-9.9-
1 − [I + S]55.4-75.6-
Table 3. Patterns of co-occurrence of small mammal species in urban habitats along the commercial axis connecting Benin and Niger. Four commensal taxa were considered: The native M. natalensis and Crocidura spp., and the invasive R. rattus and R. norvegicus. The standardized C-score (SCS) measured the extent to which pairwise taxa co-occurred: Significant negative and positive SCS indicates aggregation and segregation, respectively, whereas the SCS that does not deviate significantly from the null distribution suggests random associations. The results from two randomization models are presented: Model 1: fixed row-fixed column and model 2: fixed row-equiprobable column. Significant P-values, using the 95% confidence limit criterion, are in bold. N = 66 sites.
Table 3. Patterns of co-occurrence of small mammal species in urban habitats along the commercial axis connecting Benin and Niger. Four commensal taxa were considered: The native M. natalensis and Crocidura spp., and the invasive R. rattus and R. norvegicus. The standardized C-score (SCS) measured the extent to which pairwise taxa co-occurred: Significant negative and positive SCS indicates aggregation and segregation, respectively, whereas the SCS that does not deviate significantly from the null distribution suggests random associations. The results from two randomization models are presented: Model 1: fixed row-fixed column and model 2: fixed row-equiprobable column. Significant P-values, using the 95% confidence limit criterion, are in bold. N = 66 sites.
Species 1Number of OccurrencesSpecies 2Number of OccurrencesNumber of Joint OccurrencesModel 1Model 2
SCSPSCSP
M. natalensis55R. rattus47371.490.141.700.088
M. natalensis55Crocidura spp.4640−1.420.16−1.180.24
M. natalensis55R. norvegicus22152.910.0032.460.014
R. rattus47Crocidura spp.4633−0.330.74−0.210.83
R. rattus47R. norvegicus2221−2.620.009−2.650.008
Crocidura spp.46R. norvegicus2220−2.200.027−2.350.018
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