Land use gradients drive spatial variation in Lassa fever host communities in Eastern Sierra Leone.
Rodent
Ecology
Zoonosis
Lassa Fever
In Preparation
Landuse Change
Abstract
The natal multimammate mouse (Mastomys natalensis) is the reservoir host species of the zoonosis, Lassa fever (Lassa mammarenavirus). The spatial occurrence and abundance of this rodent species is regulated by the human environment and biotic interactions within small-mammal communities. However, little is known about these processes even in highly endemic areas of Lassa fever. Here, we conducted a rodent trapping study in a Lassa endemic region within the Eastern Province, Sierra Leone to understand how M. natalensis is distributed across a gradient of land use types and how its distribution may be influenced by the small-mammal community structure. We developed a Bayesian multi-species occupancy model using data from a multi-year trapping study (43,226 trap nights, 4 village sites, between 2020-2023) and show that, locally within study sites, M. natalensis occupancy increased along a gradient from less to more human dominated habitats (i.e., from forest through agriculture to village). However, within more intense anthropogenic land use (i.e., peri-urban settings) their probability of occupancy reduced below that of agricultural land use types. Competitive interactions with invasive rodent species within the small-mammal community may regulate the occupancy of M. natalensis, with the presence of Mus musculus, but not Rattus rattus in peri-urban settings associated with a reduced probability of occupancy of M. natalensis. This finding may help to explain past observations of lower-than-expected human cases of Lassa fever from urban settings in endemic regions. These findings highlight that land use drives spatial heterogeneity in rodent reservoir populations via both habitat and small-mammal community dynamics, with implications for the hazard of Lassa fever outbreaks. Therefore, to quantify public health risk and effectively allocate limited healthcare resources more accurate characterisation of small-mammal communities is required in regions at risk of Lassa fever outbreaks.
Motivation
This study was designed to investigate the effect of changes in landuse on the occurrence of different rodent species in a Lassa fever endemic region of Eastern Sierra Leone. While habitat preferences of Mastomys natalensis have been well studied in West Africa to better understand the risk to human populations from Lassa mammarenavirus spillover (Fichet‐Calvet et al. 2010), there is limited understanding of the wider rodent species communities. Biotic interactions between different species will drive occurrence and abundance in different habitats and this study was designed to understand how these species co-exist in these habitats.
We conducted repeated, systematic, rodent trapping in the Eastern province of Sierra Leone, along a landuse gradient to model the association of landuse and occurrence of M. natalensis and more generally small mammal communities. We aimed to investigate the following questions. First, what is the diversity of rodent communities in varied landuse types in Eastern Sierra Leone? Second, how do patterns of landuse affect the occupancy of M. natalensis and other sympatric rodents? Finally, is there evidence that the local spatial distribution of M. natalensis is regulated by biotic interactions with co-occurring species? We expect these analyses to further our understanding of rodent community structures that may explain observed patterns of Lassa fever spillover.
Method
A protocol was developed prior to a pilot trapping session in November 2020. This protocol is archived on the Open Science Framework. All field data is collected using the Open Data Kit (ODK).
Small-mammal sampling
We conducted small-mammal trapping surveys between October 2020-April 2023 within and around four village study sites (Baiama; latitude = 7.8375, longitude = -11.2683, Lalehun; latitude = 8.1973, longitude = -11.0803, Lambayama; latitude = 7.8505, longitude = -11.1969, and Seilama; latitude = 8.1224, longitude = -11.1936) in the Lassa fever endemic zone of the Eastern Province of Sierra Leone (Figure 1A.). Surveys were conducted within trapping grids along a land use gradient of anthropogenic disturbance comprising, forest, agriculture (including fallow and currently in-use areas), and villages (within and outside of permanent structures). Trapping grids were designated during the initial trapping survey session, one grid was deployed in forest land use, three to four grids were deployed in agricultural land with two grids deployed in village land use. For one village study site, Lambayama, there were no local forest areas, so this land use type was omitted (Supplementary Figure 1 A-D). Trapping survey sessions within each village occurred four times annually with two trapping surveys in each of the rainy and dry seasons (May to November and December to April, respectively), producing a total of 10 trapping sessions over the study period (Figure 1B).
Village study sites and trapping grids within the village study sites were selected to be representative of land use in the Eastern Province of Sierra Leone and based on accessibility to the sites during all seasons and acceptability of the study protocol to the village study site communities (Supplementary Text 1). The trapping protocol was as follows: at each trapping grid 49 Sherman traps (7.62cm x 8.89cm x 22.86cm) (H.B. Sherman Traps, Tallahasee, USA), were placed in a 7 trap by 7 trap grid, traps were placed 7 metres apart in a regular grid conforming to the local landscape (median trapping grid area = 4,813m2). For traps placed within permanent structures trap placement varied from this grid structure. Permanent structures were selected semi-randomly at each visit from a grid projected over the village area, with four traps placed within each structure. The location of each individual trap within trapping grids was geolocated for subsequent data processing. Traps were baited with a locally produced mixture of oats, palm oil and dried fish. Each morning the traps were checked and closed for the day prior to re-baiting during the evening. Each trapping survey session consisted of four consecutive trap-nights (TN) at each trapping grid within the village study site.
The location data of individual traps were harmonised to standardised trapping grid cells. First, a convex hull of a trapping grid across all trapping survey sessions was produced. Second, a regular grid was constructed to overlay this polygon with a grid cell size of 49m2, individual traps were allocated to these grid cells if they were contained within its borders. This produced 2,068 unique 49m2 trapping grid cells across all trapping grids and village study sites that individual traps were allocated to for all subsequent analysis (See Supplementary Figure 2 for a schematic of this process). The sf package in the R statistical computing language (R version 4.1.2) was used for geospatial manipulation and analysis (Pebesma 2018; R Core Team 2021). The four consecutive trap-nights obtained from each trap during a single survey are pooled as a single replicate for the subsequent statistical analysis.
All small-mammal handling was performed by trained researchers, animals were sedated with halothane and euthanised prior to obtaining morphological measurements and samples of blood and tissue following published protocols (Fichet-Calvet 2014). The study protocol was approved by the Clinical Research Ethical Review Board and Animal Welfare Ethical Review Board of the Royal Veterinary College, United Kingdom (URN: 2019 1949-3), and Njala University, Sierra Leone. Sex was determined based on external and internal genitalia. Images were obtained of rodents dorsal and ventral aspects. Age estimation was performed through description of each individual’s reproductive status (identification of perforate or imperforate vagina, scarring from prior embryo development, current pregnancy status or descent of testes and seminal vesicle development) and weighing of dried eye lenses. All carcasses were destroyed through incineration to eliminate the risk of onward pathogen transmission.
Species classification
Species identification was performed in the field based on external characteristics using a taxonomic key, including external morphological measurements and characteristics, following Kingdon and Happold (Kingdon and Happold 2013) and Monadjem et al. (Monadjem et al. 2015) (Supplementary Text 2). Morphological identification alone is unable to distinguish some small-mammal species within the study area at species level. Therefore, molecular identification was performed on whole blood, tissue or dried blood spots. Samples were stored at -20°C until processing, genomic DNA was extracted using QIAGEN DNAeasy kits as per the manufacturers instructions (QIAGEN 2023) (Supplementary Text 1). DNA extracts were amplified using platinum Taq polymerase (Invitrogen) and cytochrome B primers (Bangura et al. 2021). DNA amplification was assessed through gel electrophoreisis with successful amplification products undergoing Sanger sequencing. Attribution of obtained sequences to rodent species was through the BLAST programme comparing NCBI species records for rodent cytochrome B to our sample sequences (Altschul et al. 1990) (Supplementary Text 1).
Description of small-mammal detection and species community structure
Adequacy of sampling effort was assessed using species accumulation curves produced for each village study site and each land use type within a village study site, suggesting sufficient effort to detect the expected rodent species within these categories. Detection/non-detection histories for each grid cell and small-mammal species were constructed, assigning “1” when the species was detected and “0” otherwise. Species communities are described at multiple spatial scales. First, all species identified across all village sites and land use types. Second, all species identified within a village study site. Third, all species identified within a single land use type within a single village study site. We report species richness and Shannon diversity at these different spatial scales (Shannon 1948).
Estimating the effect of land use on species occurrence and richness
To adjust for differential probabilities of detection that may be driven by environmental conditions and trapping effort during the trapping study and between species, we use a Bayesian spatial latent factor multi-species occupancy model that incorporates residual species correlations, imperfect detection and spatial autocorrelation. Variable selection was informed by a pre-specified conceptual model (Supplementary Figure 4). Models were defined using the sfMsPGOcc function in the spOccupancy package in the R statistical computing language (Doser et al. 2022). This approach defines the true presence or absence (\(_{z}\)) of a species (\(_{i}\)), at grid cell (\(_{j}\)) as arising from a Bernoulli process (Equation 1). Where \({\psi}_{j}\) is the probability of occurrence of a species at a grid cell. This is modelled using a logit link where \(\beta_{i}\) are the species-specific regression coefficients of the site-specific covariates (\({\boldsymbol{x}}_{j}^{\top}\)) and a latent process \(\text{w}^*_{i, j}\). This latent process incorporates residual species correlations through a small number of latent spatial factors and latent variables representing unmeasured grid cell covariates (Equation 2). Latent spatial factors account for spatial autocorrelation using a Nearest Neighbour Gaussian Process.
\[ {z}_{i,j}\sim \mathrm{Bernoulli}\left({\psi}_{i,j}\right) \tag{1} \]
\[ \mathrm{logit}\left({\psi}_{i,j}\right)={\boldsymbol{x}}_{j}^{\top}\boldsymbol{\beta_{i}} + \text{w}^*_{i, j} \tag{2} \]
The species-specific regression coefficients (\(\beta_{i}\)) are specified as random effects arising from a common community level distribution (Equation 3). Where \(\boldsymbol{\mu}_{\beta}\) represents the community level mean effect for each occurrence covariate effect and \(\boldsymbol{T}_{\beta}\) is a diagonal matrix representing the variability of these among the species in the community.
\[ \boldsymbol{\beta}_i \sim \text{Normal}(\boldsymbol{\mu}_{\beta}, \boldsymbol{T}_{\beta}) \tag{3} \]
The detection component estimates the unobserved \({z}_{i,j}\). Here, \({y}_{i,j,k}\) is the observed detection or non-detection of a species \(i\), at site \(j\), during replicate \(k\) (Equation 4). This is approached as arising from a Bernoulli process conditional on the true latent occurrence process \({p}_{i,j,k}\). The probability of a species being detected at a grid cell, during a replicate (given it is present at grid cell \(j\)), is a function of grid cell and replicate specific covariates \({\boldsymbol{v}}\) and a set of species-specific regression coefficients \({\boldsymbol{\alpha}}_{i}\) (Equation 6).
\[ {y}_{i,j,k}\sim \mathrm{Bernoulli}\left({p}_{i,j,k}{z}_{i,j}\right) \tag{4} \]
\[ \mathrm{logit}\left({p}_{i,j,k}\right)={\boldsymbol{v}}_{i,j,k}^{\top}\boldsymbol{\alpha}_{i} \tag{5} \]
Similarly to Equation 3, these coefficients are specified as random effects arising from a common community level distribution, where \(\boldsymbol{\mu}_{\alpha}\) represents the community level mean effect for each detection covariate effect and \(\boldsymbol{T}_{\alpha}\) is a diagonal matrix representing the variability of these among species in the community (Equation 6).
\[ \boldsymbol{\alpha}_i \sim \text{Normal}(\boldsymbol{\mu}_{\alpha}, \boldsymbol{T}_{\alpha}) \tag{6} \]
Minimally informative priors were specified for community and species level coefficients (\({\alpha}\) and \({\beta}\), a normal prior of mean = 0, variance = 2.72) and for community level occurrence and detection variance parameters (\(\boldsymbol{T}_{\alpha}\) and \(\boldsymbol{T}_{\beta}\), 0.1 for the scale and shape parameters of the inverse Gamma prior).
We included covariates in the model based on a pre-specified conceptual model and after assessing for collinearity (defined as strong correlation >0.8) among variables. Continuous variables were standardised by scaling values (mean = 0, SD = 1). The fully specified model is defined in Equation 7 and 8 and using a single latent spatial factor.
\[ \text{Probability of occurrence} \sim \text{Land use type} + \text{Village} + \text{scale}(\text{Distance to permanent structure}) + \text{scale}(\text{Elevation}) \tag{7} \]
\[ \text{Probability of detection} \sim \text{scale}(\text{Monthly precipitation}) + \text{Moon fraction} + \text{scale}(\text{Number of trap nights}) \tag{8} \]
Model checks, including mixing patterns of the MCMC sampler and posterior predictive checks were performed as an assessment of goodness of fit. Bayesian p-values were produced at the community level and species level with values greater than 0.1 and less than 0.9 suggestive of adequate model fit. The Widely Applicable Information Criterion (WAIC) was used to guide final model selection (Watanabe 2010). Using this model, we estimate occupancy probability for each species in different land use types. Only estimates for species with at least 25 detections are included to avoid inference from limited data (resulting in 7 species being included in this analysis).
We drew posterior samples from the most parsimonious Bayesian occupancy model incorporating spatial autocorrelation to estimate the probability of occurrence of a species within a trapping grid cell (Supplementary Text 3). The most parsimonious model included variables for land use type and study village (Equation 9) to model the probability of occurrence. Equation 9 was used for the probability of detection component with a single latent spatial factor used.
\[ \text{Probability of occurrence} \sim \text{Land use type} + \text{Village} \tag{7} \]
Co-occurrence of Mastomys natalensis with sympatric species
To investigate the presence of competitive exclusion of the reservoir host of Lassa fever by other sympatric species within these communities we examined the correlation of the probability of occupancy of species pairs. The predicted probability of occupancy at each of the grid cells from our spatial multi-species occupancy model was obtained. We stratified these by land use type and calculated the Spearman rank correlation coefficient (\(\rho\)), conducting a two-sided test for statistical significance with a null hypothesis of no correlation between the probability of occupancy for these species. We constrain this analysis to species pairs that were detected in the land use setting in the observed data informing the model to limit inference from sparse data. Further, due to multiple statistical tests we use a conservative value of statistical significance where p <= 0.005 represents a statistically significant association to minimise the reporting of false positive associations (Benjamin et al. 2018). A statistically significant correlation was interpreted as one species being more (if a positive correlation) or less (if a negative correlation) likely to occur in a grid cell of the specific land use type if the other species were present. The causal mechanism and direction behind any observed correlations cannot be inferred from the current analysis.
Results
Small-mammal detection and species community structure
During the study period 684 individuals were detected from 43,266 trap-nights across the four village study sites (1.6% trap-success (TS)). The greatest number of individuals, highest species richness and Shannon diversity values were obtained in the agricultural areas, meanwhile, TS was greatest within village settings (i.e., within and outside of permanent structures) (Table 1). The Seilama study site had the highest overall TS, species’ richness and Shannon diversity and unlike the three other study sites had the greatest TS in agricultural areas. Species richness in Seilama was twice that of the peri-urban village study site (Lambayama) and had high Shannon diversity across all land use types. The sole peri-urban village study site (Lambayama) located within the expanding boundaries of Kenema city, had the lowest species’ richness and Shannon diversity with the majority of rodents detected within the village area.
Village | Land use | N | TN (TS %) | Species richness | Shannon diversity |
|---|---|---|---|---|---|
All villages | |||||
Village | 261 | 11516 (2.3%) | 12 | 1.67 | |
Agriculture | 379 | 26400 (1.4%) | 17 | 2.19 | |
Forest | 44 | 5350 (0.8%) | 10 | 1.78 | |
Baiama | |||||
Village | 73 | 2716 (2.7%) | 8 | 1.11 | |
Agriculture | 45 | 4696 (1%) | 9 | 1.94 | |
Forest | 3 | 1568 (0.2%) | 2 | 0.64 | |
Total | 121 | 8980 (1.3%) | 12 | 1.73 | |
Lalehun | |||||
Village | 54 | 2824 (1.9%) | 9 | 1.65 | |
Agriculture | 98 | 7608 (1.3%) | 13 | 2.18 | |
Forest | 5 | 1862 (0.3%) | 3 | 1.05 | |
Total | 157 | 12294 (1.3%) | 13 | 2.21 | |
Lambayama | |||||
Village | 93 | 2736 (3.4%) | 4 | 0.42 | |
Agriculture | 50 | 6260 (0.8%) | 6 | 1.19 | |
Total | 143 | 8996 (1.6%) | 6 | 1.03 | |
Seilama | |||||
Village | 41 | 3240 (1.3%) | 8 | 1.54 | |
Agriculture | 186 | 7836 (2.4%) | 13 | 1.97 | |
Forest | 36 | 1920 (1.9%) | 8 | 1.51 | |
Total | 263 | 12996 (2%) | 14 | 2.07 |
The most commonly detected rodent species across all land use types was M. natalensis (N = 113, 16.5%), followed by Praomys rostratus (N = 102, 14.9%), M. musculus (N = 90, 13.2%), R. rattus (N = 88, 12.9%) and Lophuromys sikapusi (N = 57, 8.3%). The insectivorous shrew species Crocidura olivieri was the most commonly detected non-rodent species (N = 105, 15.4%). Mastomys natalensis and R. rattus were detected at all village study sites, although M. natalensis was never detected in forest settings (Figure 2.). The invasive rodent species M. musculus was only detected in the Lambayama study site. The detection rate (the number of individuals detected per 1,000 TN) varied by species, land use type and village study site. The greatest rate of detection was for M. musculus in the Lambayama village study site, with the other commensal species M. natalensis and R. rattus having high detection rates across multiple village study sites within village land use types. Praomys rostratus. had the highest detection rates in forest and agricultural areas.
There was some observed variation in species detection by season. Although, the prevalence of a species (measured as the rate of detections per 1,000 TN), not accounting for incomplete detection, did not show a single trend for all species (Supplementary Figure 5A). Mus musculus had a greater detection rate in the rainy season than dry. Conversely, L. sikapusi, and Mus setulosus had greater detection rates in the dry season. The other species had similar detection rates across both seasons. There was some further variation when stratified by land use type. Mastomys natalensis had greater detection rates in villages compared to agricultural areas in the rainy season while in the dry season it was detected at similar rates in village and agricultural areas. Praomys rostratus had greater detection rates in forests during the dry season compared with the rainy season. No other species had important variations by season stratified by land use type.
Estimating the effect of land use on species occurrence and richness
We found three patterns of probability of occurrence (\(\psi\)) within a trapping grid cell for the seven included species (Figure 3. and, marginal effects of the detection parameters are shown in Supplementary Figure 6A-E). First, M. natalensis, R. rattus and M. musculus had greatest probabilities of occurrence in villages with lower occurrence probabilities in agricultural and forest areas. Mastomys natalensis differed from the two commensal, invasive species (R. rattus and M. musculus) as their probability of occurrence in agricultural settings, while lower than village settings, remained generally high. Second, P. rostratus had high probability of occurrence in forests with lower probabilities in agricultural areas and villages. Finally, C. olivieri, L. sikapusi and M. setulosus had their highest probabilities of occurrence in agricultural areas with lower probabilities of occurrence in forests and villages. No species showed high probability of occurrence across all land use types, consistent with species being adapted to distinct ecological niches.
The probability of occurrence within a trapping grid cell, within the same land use type showed wide variability for some species. For example, the narrow range of probabilities for M. natalensis in forests (0-13%) is suggestive that the probability of this species occurring within forests is low. This compares to the wide variability for P. rostratus in agricultural areas (0-90%), this suggests that additional environmental factors beyond land use type are affecting the probability occurrence.
To further explore thism we stratified village study sites by human population density into rural and peri-urban (rural <= 500 individuals per 1km2) (Figure 3.). The probability of occurrence of M. natalensis was importantly different between these settings, with high probability of occurrence in both agricultural and village settings in rural areas but substantially lower probability in peri-urban study sites. The same pattern was observed for R. rattus. For the rodent species predicted to have lower probability of occurrence in village settings, namely, P. rostratus, L. sikapusi and M. setulosus probabilities of occurrence were greater in all land use types in rural areas compared to peri-urban areas. Shrew species were predicted to have similar probabilities of occurrence in rural and peri-urban areas. Human population density itself or other environmental factors strongly associated with human population density may therefore be importantly contributing to small-mammal species occurrence.
In contrast to species found throughout our study area, M. musculus was predicted to have a low probability of occurrence in all land use types in rural areas, with high probabilities of occurrence only for village settings in peri-urban areas. The occurrence probabilities for the three commensal species (M. natalensis, R. rattus and M. musculus) suggest that competition may be a factor in reducing the occurrence probabilities of M. natalensis and R. rattus in the presence of M. musculus as in its absence these two species have high occurrence probabilities in villages.
Co-occurrence of species within land use types
The tests for species correlations show patterns consistent with the our original hypothesis that the local spatial distribution of M. natalensis is regulated by biotic interactions with co-occurring species (Figure 4.). As this analytical approach is unable to demonstrate a causal mechanism between co-occurrence and the probability of occurrence, it is nonetheless possible that unmeasured factors may be responsible for the apparent association. We observed that in land use types where both M. natalensis and M. musculus co-occurred the presence of one species was associated with a reduction in the probability of occurrence at a grid cell level of the other with a statistically significant negative correlation observed in agricultural (Spearman’s \(\rho\) = -0.67, p < 0.001) and village (\(\rho\) = -0.35, p < 0.001) settings. This negative relationship was not observed between M. natalensis and the other commensal, invasive rodent R. rattus, where a positive correlation between probabilities of occurrences in both agricultural (\(\rho\) = 0.51, p < 0.001) and village (\(\rho\) = 0.36, p < 0.001) settings was observed.
Generally, within villages, high probabilities for the presence of M. musculus were associated with lower probabilities for all other rodent species. This was not replicated for M. natalensis and R. rattus, which were positively associated with the co-occurrence of other native rodent species P. rostratus, L. sikapusi and M. setulosus. Across all land use types, the presence of the shrew species C. olivieri had a negative correlation with the presence of rodent species.
Discussion
This analysis presented the results of a systematic small-mammal trapping study in Eastern Sierra Leone investigating rodent species communities across a land use gradient in a Lassa fever endemic region. First, we found similar species richness and diversity to small-mammal sampling from other regions of the Lassa fever endemic zone in Guinea, Nigeria and Sierra Leone (Fichet-Calvet et al. 2014; Olayemi et al. 2018; Bangura et al. 2021). We found that species richness and diversity was highest in agricultural land use settings with reduced richness in both forests and villages. There was important variation of species richness and diversity between different land use types in peri-urban and rural settings. Second, the reservoir host of LASV, M. natalensis showed a response to human dominated land use with the highest probability of occupancy in villages, followed by agricultural settings and likely absence from forests. We observed similar patterns of occurrence for the two invasive, commensal, rodent species M. musculus and R. rattus. Following stratification by human population density, we found that the probability of occurrence of M. natalensis was lower in peri-urban settings where M. musculus has replaced M. natalensis as the dominant commensal rodent species. Finally, we assessed for correlations in the probability of co-occurrence. We found a negative association between the probability of occupancy of M. natalensis and M. musculus within villages that could have important implications for the understanding of risk of spillover of Lassa fever in endemic regions.
Small-mammal communities are associated with land use type
Small-mammal species richness was found to be greatest in agricultural settings. In these settings both synanthropic and non-synanthropic species were found, resulting in increased species richness and diversity. Agricultural land use may thus provide greater opportunity for LASV transmission among species within diverse small-mammal communities.
There is some evidence for a role of the wider species community in LASV transmission. Current or prior infection with LASV (through detection of virus or antibodies), has been identified in 11 additional small-mammal species to the reservoir species, whether these represent incidental infections or competent chains of viral transmission are not known (Monath et al. 1974; Demby et al. 2001; Fichet-Calvet et al. 2014; Olayemi et al. 2016; Simons et al. 2023). It is possible that viral sharing within small-mammal communities is greatest in these species rich agricultural settings, allowing introduction or re-introduction of the pathogen into isolated commensal species populations following local extinction of virus (Bordes, Blasdell, and Morand 2015). This may be particularly important for maintaining viral persistence in spatially isolated M. natalensis populations, as rapid depletion of susceptible individuals is expected in isolated well mixed populations (Goyens et al. 2013). The spatial isolation of communities of this species is supported by this study and previous studies finding an absence of M. natalensis within forested regions and limited geographic dispersal (Leirs, Verheyen, and Verhagen 1996; Denys et al. 2005; Mariën et al. 2018). The role of the wider rodent community in translocating LASV between M. natalensis populations needs further investigation.
Mastomys natalensis was not detected more frequently in village settings during the dry season, as has been reported from elsewhere in Sierra Leone and Guinea (Fichet-Calvet et al. 2007; Bangura et al. 2021). The finding of similar or increased prevalence of M. natalensis between seasons, not accounting for imperfect detection, were consistent across village study sites. It is possible that in this region different agricultural processes or food storage practices by village communities results in different rodent behaviour to elsewhere in its range (Kelly et al. 2013; Leach et al. 2017). Alternatively, increased trap-shyness during periods of increased abundance may mask replication of previous findings. Additional small-mammal community studies, incorporating local human community behaviour and practices, over longer time periods, and across different geographic regions, would be informative to ascertain seasonal habitat preferences of these rodents. It has been suggested that prevalence of M. natalensis within households is the proximal driver of the risk of Lassa fever spillover into human populations driven by the potentially increased rate of human-rodent host contact within households (Bonwitt et al. 2017; Mariën et al. 2020). Therefore, migration from species rich agricultural settings to within households of infectious hosts of LASV may be an important component of Lassa fever outbreaks.
Evidence for biotic interactions shaping patterns of small-mammal species diversity
The finding that small-mammal species displayed segregation into distinct ecological niches of human dominated (village and agriculture) or non-human dominated (forest) land use types suggests an important role for biotic factors in species occurrence. The high predicted occupancy of both M. natalensis and R. rattus in human dominated landscapes and positive correlation in co-occurrence is consistent with another study conducted elsewhere in Sierra Leone (Bangura et al. 2021). This suggests that these two rodent species may not directly compete for resources and that the presence of one species does not preclude the other. This may not be true for interactions between M. musculus with both R. rattus and M. natalensis. The probability of occurrence of M. musculus within villages was negatively correlated with the co-occurrence of both M. natalensis and R. rattus. Mus musculus was also absent in village land use types in rural settings where R. rattus and M. natalensis had high probabilities of occurring. This trend was replicated for M. musculus with all other rodent species in village settings. It is not possible to ascertain from the current study whether M. musculus is expanding into rural settings and what effect this may have on small-mammal communities and LASV transmission. To identify the causal processes of changes in small-mammal species community structures in response to invasive species’ range expansion longer term monitoring of small-mammal communities would be beneficial, similar to that conducted in Senegal (Dalecky et al. 2015).
Benefits and challenges of systematic small-mammal community sampling
Systematic investigation of small-mammal communities requires a greater amount of sampling effort compared to targeted sampling of a single species within selected habitats. In this study we had a low overall trap success rate compared to other studies focused on synanthropic rodent species’ (Olayemi et al. 2018; Bangura et al. 2021; Happi et al. 2022). The obtained trap success rate of 3.3% within villages is comparable to the 3% obtained from a study conducted in Bo, Sierra Leone, but is substantially lower than the 17% and 14% reported from Nigeria and Guinea respectively (Fichet-Calvet et al. 2007; Happi et al. 2022). Detection rates of M. natalensis within its Western radiation of Nigeria, Guinea and Sierra Leone are also lower than that obtained from Tanzania where trap success rates around 24% are reported from agricultural settings (Mulungu et al. 2013). Whether this represents different behaviour within the species based on food availability is not known, although environmental food availability is known to be associated with trap-shyness (Taylor, Hammond, and Quy 1974; Stryjek, Kalinowski, and Parsons 2019). Despite the increased trapping effort required to obtain the necessary number of detections for statistical inference, adopting this approach will mitigate some of the biases in small-mammal species and viral detection introduced by less systematic, more targeted sampling.
Comparison between studies using different sampling techniques and study designs presents several challenges. Previous studies on rodent communities in the Lassa fever endemic region have used trap success rates as an indirect measure of rodent abundance in the absence of capture-mark-recapture studies (Fichet-Calvet et al. 2009; Olayemi et al. 2018; Bangura et al. 2021). Our analysis, using a model incorporating imperfect detection, suggests estimating abundance from trap success may not be applicable across different land use types and species (Supplementary Figure 6C). For example, we found that the probability of detection of M. musculus and R. rattus were higher than native species given a consistent amount of trapping effort. Detection rate as a measure of relative abundance has been shown to poorly replicate using combined live-trapping and camera-trapping approaches in the USA (Parsons, Clark, and Kays 2022). Drivers of variability in detection may include trap-shyness (i.e., neophobia) of non-synanthropic species, the availability of resources in the local environment and the placement locations of traps, the contribution of these factors on detection require further study in our setting (Stryjek, Kalinowski, and Parsons 2019). Improved harmonisation of rodent sampling designs particularly incorporating systematic small-mammal community sampling will facilitate direct comparison of small-mammal species communities and pathogen prevalence across the Lassa fever endemic region.
There are several limitations to the current study. Animal sampling was limited to a relatively short period, less than three years, it is possible that populations in these settings have important multi-year variations in abundance that could not be captured in the current model and therefore the probability of occurrence may be under-estimated for species that were at low abundance during our survey period. Sampling over a longer time period would allow any potential temporal changes in probability of occurrence to be better identified. Similarly, land use in Sierra Leone, particularly agricultural land, goes through multi-year cycles of use. It would be informative to study a single location in the typical transition from forested, to agricultural, to long term fallow and to degraded forest land use to better characterise changes in rodent communities within these settings of land management. Unobserved characteristics of our study villages also likely contributed to the composition of rodent communities, suggested by the wide posterior distributions for some rodent species, expanding this study to sample more villages would be beneficial to allow further generalisation of our findings over the wider region.
Implications for understanding the risk of Lassa fever spillover
The lower levels of occurrence of M. natalensis in agricultural and forest land use is consistent with increasing evidence of LASV prevalence heterogeneity across the endemic region (Mariën et al. 2020). In some village communities within the endemic region no evidence of current LASV transmission has been observed within the rodent populations, despite prior human cases or serological evidence of outbreaks, suggesting important temporal and spatial variation in pathogen prevalence in rodents (Bangura et al. 2021; McCormick et al. 1987; Leski et al. 2015). As discussed above it may be that transmission among the rodent community is short lived with rapid local extinction of LASV (Goyens et al. 2013). These phenomena may implicate non-M. natalensis species as being important for transferring the pathogen between communities of M. natalensis resident in villages separated by forest, or other land use types not colonised by M. natalensis, leading to pathogen re-introduction. Several species found to occur in forest settings have been found to have antibodies against LASV, namely Praomys rostratus, M. setulosus, Malacomys edwardsi and L. striatus (Monath et al. 1974; Demby et al. 2001; Fichet-Calvet et al. 2014; Olayemi et al. 2016; Simons et al. 2023). To understand the temporal and spatial variability in LASV prevalence small-mammal sampling and movement ecology studies across the wider land use gradient are required.
Finally, current disease models of Lassa fever risk do not account for the involvement of multiple rodent species or biotic interactions between species (Basinski et al. 2021; Mylne et al. 2015; Olugasa et al. 2014; Redding et al. 2016; Fichet-Calvet and Rogers 2009; Klitting et al. 2022). The finding of interactions between M. natalensis and primarily M. musculus may indicate that Lassa fever risk could be reduced in settings where M. musculus is present. Further research exploring the competence of M. musculus as a host of LASV are required, as evidence of prior infection, through serological assays has been reported (Demby et al. 2001). If M. musculus is not a competent host of LASV this may go some way to explain why Lassa fever is more typically reported from rural locations in the endemic region rather than cities, where this invasive species may have displaced more competent viral hosts. Further work systematically sampling across the urban-rural gradient will be required to test this hypothesis which will have implications for estimates of future Lassa fever risk. West Africa continues to undergo large population growth and rapid urbanisation, so the expansion of M. musculus may therefore moderate the risk of increasing numbers of Lassa fever outbreaks.
Statement on inclusion
This study brings together authors from several countries, including scientists based in the country where the study was carried out. All authors were engaged early on with the research and study design to ensure that the diverse sets of perspectives they represent was considered. Literature published by scientists from the study country and wider region was cited. Study protocols were discussed with local scientists for appropriateness of design. Consultations were held with community leaders of the study villages and the wider region before finalising the study design and enrolment of sites into the study.
Data availability and draft manuscript
Data are available in the project’s GitHub repository. The most recent version of the draft currently being shared with co-authors is V3.
Additional discussion
An interactive report has been produced for collaborators, it may take some time to load.
I presented data from the first year of trapping at the 2022 Ecology and Evolution of Infectious Diseases conference, the slides are available and the talk is embedded below.
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Citation
BibTeX citation:
@online{simons2023,
author = {Simons, David},
title = {Land Use Gradients Drive Spatial Variation in {Lassa} Fever
Host Communities in {Eastern} {Sierra} {Leone.}},
date = {2023-10-01},
url = {https://www.dsimons.org/lassa/rodent_trapping.html},
langid = {en},
abstract = {The natal multimammate mouse (*Mastomys natalensis*) is
the reservoir host species of the zoonosis, Lassa fever (*Lassa
mammarenavirus*). The spatial occurrence and abundance of this
rodent species is regulated by the human environment and biotic
interactions within small-mammal communities. However, little is
known about these processes even in highly endemic areas of Lassa
fever. Here, we conducted a rodent trapping study in a Lassa endemic
region within the Eastern Province, Sierra Leone to understand how
*M. natalensis* is distributed across a gradient of land use types
and how its distribution may be influenced by the small-mammal
community structure. We developed a Bayesian multi-species occupancy
model using data from a multi-year trapping study (43,226 trap
nights, 4 village sites, between 2020-2023) and show that, locally
within study sites, *M. natalensis* occupancy increased along a
gradient from less to more human dominated habitats (i.e., from
forest through agriculture to village). However, within more intense
anthropogenic land use (i.e., peri-urban settings) their probability
of occupancy reduced below that of agricultural land use types.
Competitive interactions with invasive rodent species within the
small-mammal community may regulate the occupancy of *M.
natalensis*, with the presence of *Mus musculus*, but not *Rattus
rattus* in peri-urban settings associated with a reduced probability
of occupancy of *M. natalensis*. This finding may help to explain
past observations of lower-than-expected human cases of Lassa fever
from urban settings in endemic regions. These findings highlight
that land use drives spatial heterogeneity in rodent reservoir
populations via both habitat and small-mammal community dynamics,
with implications for the hazard of Lassa fever outbreaks.
Therefore, to quantify public health risk and effectively allocate
limited healthcare resources more accurate characterisation of
small-mammal communities is required in regions at risk of Lassa
fever outbreaks.}
}
For attribution, please cite this work as:
Simons, David. 2023. “Land Use Gradients Drive Spatial Variation
in Lassa Fever Host Communities in Eastern Sierra Leone.” October
1, 2023. https://www.dsimons.org/lassa/rodent_trapping.html.