Skip to main content
NCBI home page
Zoological Studies logoLink to Zoological Studies
. 2022 Sep 29;61:e47. doi: 10.6620/ZS.2022.61-47

Use of Habitat Suitability Model as a Tool to Highlight Best Conservation Area for the Red-bellied Monkey (Cercopithecus erythrogaster erythrogaster) in Southern-Benin, West Africa

Omobayo Ghislain Zoffoun 1,2,*, Côme Agossa Linsoussi 2, Georges Nobimè 2, Augustin Sinsin 2
PMCID: PMC9745574  PMID: 36568805

Abstract

Wildlife habitats are increasingly degraded as a result of anthropogenic pressures. The IUCN recently updated the red list category of the red-bellied monkey (Cercopithecus erythrogaster erythrogaster) from Endangered to Critically Endangered due to its population decrease, habitats degradation and various threats to its conservation. It is therefore important to identify areas of great importance for the sustainable conservation of the subspecies. The Species Distribution Model (SDM) is a method increasingly used by conservationists to help find these areas and thus limit areas of intervention. In this study, maximum entropy model was used to identify suitable habitats for the red-bellied monkey in landscape of southern-Benin from occurrence data and selected predictor variables according to ecological habitat requirements of the subspecies. The suitable habitat model for the red-bellied monkey has a good predictive power (AUC = 0.97). The variables that contributed most to the final model, as indicated by the permutation importance, were: Distance to Water (47.7%), Land Cover Class (23.1%), Brightness (17.0%), Wetness (4.7%), Human Population Size (2.8%) and Elevation (2.2%). Thus, using Maximum Training sensitivity and Specificity threshold, 3.62% of the landscape was classified as suitable and 96.38% was classified as unsuitable for the red-bellied monkey. The largest area of suitable habitat is found in protected areas (57.46%), mainly in the Lama Forest Reserve central core (49.5%). The landscape is fragmented and 91.49% of suitable habitats are between 0 and 0.01 km2 in size. The mean size of suitable habitats in the landscape is 0.017 ± 0.545 km2. Nevertheless, there is no significant difference between the mean size of suitable habitats in protected areas and those in the unprotected area (P = 0.061, Mann-Whitney U tests). The Average Nearest Neighbor Distance of suitable habitats in the landscape is low (0.139 km) and the Average Nearest Neighbor Ratio (R) is less than 1 (R = 0.408, p < 0.001). Those features indicate a clustered pattern of suitable habitats for the red-bellied monkey in the landscape. This makes it possible to foresee the establishment of connections between the isolated suitable habitats and thus allow for the long-term conservation of the species populations.

Keywords: Red-bellied monkey, Species Distribution Model, Suitable habitat, Protected areas, Anthropogenic pressures

BACKGROUND

In Africa, non-human primate populations have been most affected by the loss and alteration of forest habitats resulting from factors such as intensive logging, shifting cultivation, wildland fire, excessive cutting of firewood to commercial and domestic purposes, pasture creation and clearing for crop establishment (Estrada et al. 2017; Badiella-Giménez et al. 2021). Bergl et al. (2008) noted that primates are particularly vulnerable to deforestation because of their dependence on tropical forest habitats and low reproductive rates. Therefore, a better understanding of environmental factors such as forest cover distribution and species distribution modelling would be vital to improve the conservation and appropriate management of forest protected areas in Africa.

Benin, a country in West Africa, is home to a diversity of eleven primates species (Nobimè et al. 2010), including the red-bellied monkey (Cercopithecus erythrogaster erythrogaster) subspecies endemic to Dahomey Gap (Nobimè 2012). Cercopithecus erythrogaster erythrogaster is listed as Critically Endangered by the International Union for the Conservation of Nature (Goodwin et al. 2020) and is listed in Appendix II of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (UNEP-WCMC 2014). According to Sinsin et al. (2000), suitable habitats for the red-bellied monkey in Benin are highly fragmented. Pressure from farmers who continue to cut trees for firewood and clear land for agriculture is predicting a continuing decline in the area of occupancy of the red-bellied monkey (Nobimè et al. 2009). Large area of the species range then appears to be unoccupied and those portions where the habitats are suitable and the species are truly present are not equivalent. It is therefore important to determine what proportion of the suitable habitats are found in protected areas and in what part of the protected area they are (Rodrigues et al. 2003). In order to develop species conservation plans in areas threatened by deforestation and habitat change, it is vital to collect data on their habitat preferences, as well as on the availability and location of suitable habitats (Estrada et al. 2017).

In research, collecting extensive data on a large scale can be very costly, time-consuming, if not impossible. Available field data are often insufficient to meet all conservation needs (Polasky et al. 2000; Wilson et al. 2005). This difficulty has led biologists to develop methods of modelling the distribution of taxa over the last thirty years. On the one hand, they make it possible to better understand the potential distribution of a species and its habitats and, on the other hand, these models help researchers better understand conservation issues. The Species Distribution Model (SDM) can provide information on the potential distribution of a species when occurrence data are insufficient, and can assist with conservation planning (Guisan and Zimmermann 2000) by highlighting unknown populations (Pearson et al. 2007), suitable sites for reintroduction (Hernandez et al. 2006), and key areas for fieldwork (Papes and Gaubert 2007), improve the assessment of threat status (Solano and Feria 2007). This method will then enable us to identify suitable habitats for red-bellied monkeys, which is necessary in conservation ecology for the protection of species.

This study aims to contribute to a better assessment of the conservation status of the red-bellied monkey in Benin. The specific objectives were to (1) identify precursor habitat variables suitable to the red-bellied monkeys, (2) identify suitable habitats for the red-bellied monkey in the study area in southern Benin, and (3) analyze the spatial structure of suitable habitats for the red-bellied monkey.

MATERIALS AND METHODS

Study area

This study was carried out in southern Benin, a region characterized by a subequatorial climate with two dry seasons (a small one from August to September, the large one from December to March) and two rainy seasons (a large one from April to July and a small one from September to November) and is found in the Guineo-Congolian phytogeographic area. The study area covers an area of 1997 km2 (Fig. 1) and covers four administrative regions: the Zou, the Atlantic, the Ouémé and a very small part in the Couffo. The average rainfall of the study area recorded at the Bohicon synoptic station by ASECNA was 1131 mm for the period ranging from 1988 to 2018. The average minimum temperature for the same period is 28°C with maximums of 37°C and minimums of 21°C. The relative humidity is 65% on average and ranges from 25% to 97%. Specifically, four forests where the red-bellied monkey has been previously reported as present (Campbell et al. 2008; Nobimè et al. 2009) were prospected and represent the survey sites: the Lama Forest Reserve (16,250 ha), the Lokoli swamp forest (3000 ha), Gnanhouizounmè forest (6 ha) and the Togbota forest (2 ha) (Fig. 1). The landscape including the four forests object of the present study is the preferred area of the red-bellied monkey in Benin (Sinsin et al. 2002) and is, therefore, our study area. In the study area there are three protected areas: Lama Forest Reserve, Agrimey Forest Reserve and Djigbé Forest Reserve. The Lama Forest Reserve extends between 6°55' and 7°00' North and between 2°04' and 2°12' East and covers an area of 16,250 ha. But the portion of semi-deciduous dense forest, commonly called the Central Core, in this area covers only 4,785 ha (ONAB 2011). About 10,046 ha of the Lama Classified Forest are covered by teak plantations and 1,418.83 ha by resettlement perimeters for local populations, which are used for housing and agricultural production. The Agrimey Forest Reserve extends between 7°1' and 7°4' North and between 2°2' and 2°12' East. The forest covers an area of 2624.39 ha, including 2495.38 ha of mainly teak plantation for the production of timber, 124.67 ha of natural vegetation and 4.34 ha on which infrastructure is located (ONAB 2005). The Djigbé Forest Reserve extends between 6°81' and 6°91' North and between 2°29' and 2°36' East. The forest covers an area of 3720.80 ha, including 3616.70 ha of mainly teak plantation for the production of timber, 102.06 ha of natural vegetation and 2.043 ha on which infrastructure is located (ONAB 2005).

Fig. 1.

Fig. 1.

Open in a new tab

Location of the study area in Southern Benin (West Africa). The red points represent visual contacts GPS locations of the red-bellied monkey (N = 22) used in this study. Source: Field work, 2019 and Benin IGN Map, 2018. Spatial reference system: WGS 1984 UTM Zone 31.

Data collection

Occurrence points for the Species Distribution Model (SDM) for the red-bellied monkey

The occurrence points used for the modeling of suitable habitats for the red-bellied monkey are those resulting from the counts of the red-bellied monkey in southern Benin taking place in 2019 in four forests: Lama Forest Reserve (16,250 ha), the Lokoli swamp forest (3000 ha), Gnanhouizounmè forest (6 ha) and the Togbota forest (2 ha). In Lama Forest Reserve, data were collected along 07 linear transects with a total length of 57.163 km, existing in the central core of the Lama Forest Reserve (Fig. 1) representing the tracks already traced by ONAB (Benin National Wood Office), the structure responsible for the management of the forest. Each transect crosses a diversity of forest vegetation, such as: typical dense forests, secondary forests, teak plantation areas, fallow land with Chromolaena odorata and former settlements area dominated by Elaeis Guineensis. They are all parallel and separated from each other by a distance about 1 km. The length of the transects varies between 3.98 and 9.04 km. The transects were walked in October 2019 at an average speed of around 1.25 km/h (Peres 1999). Surveys on the different transects occurred at 6 a.m. to 8 a.m. and from 4 p.m. to 6 p.m. (Peres 1999). For each observation of red-bellied monkey groups, the geographic coordinates using a GPS Garming 64 st, the type of habitat, the number of individuals, the height of the animal in the tree, and the species of the tree are noted. In the Togbota and Gnanhouizoumè forests, the line transect method was also used to collect occurrence data. Three (3) transects of 400 m were surveyed in Gnanhouizoumè forest and tree (3) transects of 250 m in Togbota forest. In Lokoli swamp forest, navigable only by canoe, the data were collected along navigable tracks of 12.505 km traversing the forest in the length with our canoes. Twenty-two (22) occurrence points were collected during fieldwork. This small number of occurrence points resulting from the fieldwork is due to the ability of the red-bellied monkey to quickly hide under the undergrowth and thus makes difficult to observe the subspecies in the field. The occurrences data was inspected to remove occurrence points that fell within the area of a single pixel of 900 m2. However, after this inspection, no two occurrence points fall within a single pixel of 900 m2, so number of occurrence points then remained 22.

Predictive biophysical variables for SDM

Raster layers of predictor variables dealing with landscape structure and land cover, called biophysical variables, were prepared at a spatial resolution of 30 meters. A total of 13 variables (Table 1) were generated: Land Cover Class (LCC), Normalized Difference Vegetation Index (NDVI), photosynthetically active vegetation (Greenness), soil moisture (Wetness), soil and surface luminosity (Brightness), Elevation, Aspect, Slope, Heat Load Index (HLI), Compound Topographic Index (CTI), Distance to Water (DW), Distance to Main Roads (DMR) and Human Population Size (PS).

Table 1.

Variables used to model habitat suitability of red-bellied monkey in response to the heterogeneity of the Southern Benin Forest landscape. After performing a correlation analysis, the 11 variables selected for use in the final model are marked with *

graphic file with name zoolstud-61-047-t001.jpg

Open in a new tab

A supervised classification of a Landsat 8 OLI /TIRS image of December 2018 was carried out with the Semi-Automatic Classification Plugin (Version 6.3.1) for QGIS 3.6 to define the Land Cover Classes (LCC) using the maximum likelihood method (Congedo and Munafò 2012). The seven Land Cover Classes identified were Dense Forest, Mixed Forest, Forest Plantations, Farms and Fallows, Water, Settlement and Swamp.

Vegetation indices were then calculated to capture differences at the micro scales because primates are reputed to have mental mapping abilities capable of perceiving their environment at the level of individual trees and forest plots (Ban et al. 2014; Fitzgerald et al. 2018). To do this, a Normalized Difference Vegetation Index (NDVI) raster was generated from the Landsat 8 OLI /TIRS image. NDVI is an indicator of biomass (i.e., healthy, photosynthetically active vegetation) within each raster cell and can range from -1 (water or bare soil) to 1 (dense and healthy vegetation). It is calculated using the near-infrared and red bands of a satellite image ((NIR -R) /(NIR + R)) (Campbell and Wynne 2011).

In addition, the characteristics of the micro-habitats in the study area were captured using a tasseled cap transformation from the original Landsat 8 OLI /TIRS image (ToA value i.e., Top of Atmosphere reflectance) (Ali and Salman 2016). This process transforms the original spectral data into a new coordinate system with six orthogonal axes. The first three axes that were used represent the following indexes: (1) the luminosity of the soil and the surface (Brightness), (2) the photosynthetically active vegetation (Greenness), and (3) the soil humidity (Wetness) (Serckx et al. 2016). These are good indicators of how climate and vegetation can play an important role in determining suitable habitat. Wetness is an important climatic factor for the red-bellied monkey presence (Kassa et al. 2007; Nobimè et al. 2009).

According to Sinsin et al. (2002), the red-bellied monkey is only present in natural vegetation where the topographic situation and the edaphic conditions make it possible to keep the water on the surface periodically and to maintain a high humidity throughout the year. Therefore, the following topographic variables included in the initial model were chosen for their ability to serve as explanatory variables for this factor. These variables were generated using QGIS v.3.6.3 and derived from a Digital Elevation Model (DEM) with a resolution of ~30 m from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) version 3 (NASA 2018): Elevation, Slope and Aspect. Aspect is the direction a slope faces (Flat, North, South, West, East, Northeast, Northwest, Southeast, Southwest).

The sighting areas of the red-bellied monkey population are characterized by wet habitats: swamp forests, forest galleries and dense semi-deciduous forests that flood during the rainy season (Kassa et al. 2007; Nobimè et al. 2009). For this purpose, the variable Distance to Water (DW) was generated in QGIS v.3.6.3 as the euclidean distance of each raster cell of 30 × 30 m to the nearest area with water. In addition, two topo climatic indexes derived from DEMs were added to the other variables for their ability to determine wetlands characteristic of the red-bellied monkey habitat: Heat Load Index (HLI) and Compound Topographic Index (CTI). The first, Heat Load Index (HLI), provided a relative indication of temperature experienced on the ground, and the second, Compound Topographic Index (CTI), describes relative variation in water availability. HLI relates to evapotranspiration rates and soil temperatures and is a direct measure of incident radiation (McCune and Dylan 2002; Evans et al. 2014). CTI is a metric of potential ground wetness that is considered steady-state, or based on variables that remain relatively constant over time. CTI models water flow accumulation as a function of upstream contributing area and slope (calculated by percent rise). Prolonged exposure to water is a key factor in determining soil type, and CTI has been shown to be strongly correlated with many soil properties, including depth, texture, organic content and moisture (Moore et al. 1993; Evans et al. 2014). All the variables derived from the DEMs were finally resampled at 30 × 30 m using the bilinear method.

Since human settlement development can induce disturbance and persecution that negatively affects the habitat of primates (Estrada et al. 2017), we also generated a raster with lowest Distances to the Main Roads (DMR), provided by IGN Benin (National Geographic Institute of Benin), as well as the Population Size (PS) i.e., persons per grid square, downloaded from https://www.worldpop.org/doi/10.5258/SOTON/WP00023 at a resolution of ~100 m and resample at 30 m.

Data analysis

Species Distribution Modelling technique

First, the correlation among the predictive variables was examined in order to reduce the effect that collinearity could have on the interpretation of Maxent’s results (Kumar et al. 2014). The Pearson (r) correlation was used for this purpose in R 3.5.3 (R Development Core Team 2019). For a set of highly correlated variables (| r | > 0.7, P < 0.05), the variable with the strongest predictive power in the preliminary model using all the 13 initial variables was chosen (Estes et al. 2010) (Table 1).

To map the suitable habitats for the red-bellied monkey in the study area and analyze the biophysical variables contributing to their adequacy, we used a software that only works with presence data, namely Maxent 3.4.1 software based on maximum entropy (Phillips et al. 2017). Maxent’s algorithm has been shown to work well with presence data only and often outperforms other SDM methods (Elith et al. 2011; Wilson et al. 2013). Maxent estimates the relative probability of species presence from occurrence data (Fig. 1) and user-selected predictor variables (Phillips et al. 2006) (Table 1, Fig. 2). The result is a perfectly adapted model for classifying the locations in the study area according to the probability of presence (0 to 1, with 1 indicating the highest probability of presence).

Fig. 2.

Fig. 2.

Open in a new tab

Logic model representing the model inputs used to find suitable habitat for the red-bellied monkey in the study area in southern Benin.

The predictive performance of the model is evaluated using the Area Under Curve (AUC). The AUC was chosen over other assessment measures because it does not require arbitrary threshold selection (Phillips et al. 2006). For presence data only, the AUC describes the probability that the model assigns a presence site a higher score than that of another site in the study area (Phillips et al. 2009). The performance of the model can be classified into three categories based on the value of AUC: fair (0.7–0.8), good (0.8–0.9) and excellent (0.9–1.0) (Phillips et al. 2006).

To use the model in Maxent, we relied on recommended default values for the convergence threshold (10-5) and maximum number of iterations (1000) (Phillips and Dudik 2008). The species presence data were randomly divided into 75% as the training dataset and 25% as the validation dataset. A cross-validation procedure was replicated 10 times to account for uncertainty introduced by training and validation set splits and to obtain an average AUC value for the final model (Kumar et al. 2014). Since formal absence data were not available, a maximum of 10,000 background points was randomly generated to represent the availability and range of environmental conditions within the study area (Wilson et al. 2013). However, to limit the generation of background points only to the area in which occurrence data were collected—i.e., in the different prospected forests—a minimum convex polygon around the occurrence points was created. This ensures that the sampling of the background points is limited to the same region from which the occurrence points were collected and helps account for sampling bias (Phillips et al. 2009).

The logistic outputs of habitat suitability were converted into binary outputs of unsuitable and suitable habitats using the threshold of Maximum Training Sensitivity and Specificity (Max TSS) as explained for the model generated employing presence-only data by Liu et al. (2013). According to Liu et al. (2013), Maximum Training Sensitivity and Specificity (Max TSS) is a promising method for threshold selection when only presence data are used and is more accurate than over methods.

Besides, Maxent’s results generate response curves showing the relationship between each predictor variable and the predicted probability of the red-bellied monkeys’ presence, and the importance of permutation is reported for each variable. The importance of permutation is the contribution of each variable to the final model and is a measure of how the AUC changes when a variable is removed from the model and is not sensitive to the order in which the variables are inserted into the model (Wilson et al. 2013; Fitzgerald et al. 2018).

Analysis of the spatial structure of suitable habitats using fragmentation parameters

To analyze the spatial structure of suitable habitats to red-bellied monkeys in our study area, we estimated the following parameters: (1) area of suitable habitats, (2) number of fragments, (3) the mean size of the fragments and (4) the Average Nearest Neighbor Distance and Average Nearest Neighbor Ratio (R) (Moore and Carpenter 1999). An R value less than 1 indicates that the distribution is clustered and a value more than 1 indicates that distribution is dispersed or uniform. R = 1 indicates a completely random distribution pattern. In addition, the spatial distribution of suitable habitat fragments was analysed by pair correlation function g(r) following Pebesma and Bivand (2005) using spatstat package under R 3.5.3 (R Development Core 2019). Finally, the mean size of the suitable habitats fragments in the protected areas was compared to those on unprotected areas using the Mann-Whitney U test under R 3.5.3 in order to assess the contribution of protected areas in the conservation of wildlife habitats, especially red-bellied monkey.

RESULTS

Habitat suitability model for the red bellied monkey

After checking the correlation between the variables, three pairs of variables were found to be highly correlated (| r | > 0.7, P < 0.05): Wetness and NDVI (r = 0.75), Greenness and NDVI (r = 0.98), and Greenness and Wetness (r = 0.73). The variable with the lowest permutation importance for the contribution to the model of the 13 starting variables was removed for each highly correlated pair. Finally, 11 variables were selected for the final model: Wetness, Brightness, Land Cover Class (LCC), Elevation, Aspect, Slope, Heat Load Index (HLI), Compound Topographic Index (CTI), Distance to Water (DW), Distance to Main roads (DMR) and Population Size (PS) (Table 1).

The model of red-bellied monkey habitat suitability performed well, as indicated by the high AUC of 0.974, which indicates that the potential distribution of the subspecies fits well with our data and is therefore ecologically useful. The final model was classified according to the probability of presence and is presented in figure 3A. Then, the habitat suitability model was converted to the binary outputs of unsuitable and suitable habitats using the threshold of Maximum Training Sensitivity and Specificity (Max TSS = 0.0898) (Fig. 3B). Thus, 3.62% of the landscape was classified as suitable and 96.38% was classified as unsuitable for the red-bellied monkey. The variables that contributed most to the final model, as indicated by the importance of the permutation, were: Distance to Water (47.7%) (this variable contributed the most to the model), Land Cover Class (23.1%), Brightness (17.0%), Wetness (4.7%), Population Size (2.8%) and Elevation (2.2%) (Table 2). These variables contributed to 97.5% of the final model and are therefore the precursors of the presence of the red-bellied monkey in the intervals of the values of these variables inducing a high probability of presence (Fig. 4A to 4F). The HLI variable did not contribute to the model (0%) and the Distance to Main Roads, Aspect, Slope, and Compound Topographic Index variables contributed very little (< 2%), and the response curves of these variables are not presented.

Fig. 3.

Fig. 3.

Open in a new tab

Probability of red-bellied monkey presence (A) and suitable habitats (upper 0.0898 of red-bellied monkey presence probability) identified with the Maximum Training Sensitivity and Specificity (Max TSS) threshold (B).

Table 2.

Permutation importance of each biophysical predictor variable used to create the final habitat suitability model

graphic file with name zoolstud-61-047-t002.jpg

Open in a new tab

Fig. 4.

Fig. 4.

Open in a new tab

Plots of the response curves for each variable depending on the probability of presence and map of their spatial distribution. Each plot represents a Maxent model using only the corresponding variable. The plots are given for the six biophysical variables (A to F) with highest permutation importance > 2% (percent shown on plot). The plots show the average response (red line) and the standard deviation (blue interval around the average). For Land Cover Class, two shades (blue and green) represent upper and lower limit defined by standard deviation.

The response curves of the Maxent model provides information on the relationship between the probability of presence (probability of suitable habitat) and the input biophysical variables. The spatial distribution of the variables is displayed above each response curve (Fig. 4A to 4F). After analyzing the response curves, it appears that the high probability of presence of the red-bellied monkey is obtained for area with the low Distance to Water (0–2000 m). The Land Cover Class inducing the greatest probability of the red-bellied monkey presence is Dense Forest (0.68) followed by Mixed Forest (0.08) and the one inducing the lowest probability of presence is Farms and Fallow (0.01). As for Brightness, low value (0.28–0.37) induced the high probability of presence. There is a strong positive correlation between the probability of presence and Wetness. Indeed, positive great wetness value (0.02–0.1) induced a high probability of presence. Besides, the probability of red-bellied monkey presence and Population Size evolves in two diametrically opposite directions. When the number of people in each 900 m2 grid is close to 0 the probability of presence is high (between 0.5 and 0.9) then decreases as the number of people tends to move away from 0, and when the number of people per grid reaches 5 the probability of presence cancel. Also, the probability of presence is high for positive low elevation value between 40 and 100 m.

Spatial structure of suitable habitats

The largest area of suitable habitats was found in protected areas (57.46%) (Fig. 5 and Table 3) and mainly in the Lama Forest Reserve and particularly in its central core with 35.81 km2 so 49.5% of the total area of suitable habitats. In addition, 18.06% of the total area of protected areas are suitable and 1.74% of total area of unprotected areas are classified as suitable habitat for the red-bellied monkey (Table 3). The landscape is fragmented with a total of 4279 suitable habitats, among which 91.49% are between 0 and 0.01 km2 in size (Table 4 and Fig. 5). The mean size of suitable habitats in the landscape was 0.017 ± 0.545 km2 with 0.045 ± 1.156 km2 in protected areas and 0.009 ± 0.086 km2 in unprotected areas. Nevertheless, there is no significant difference between the mean size of suitable habitats in the protected areas and those in the unprotected area (P = 0.061, W = 1502000, Mann-Whitney U test). The Average Nearest Neighbor Distance of suitable habitats in the landscape was low (0.139 km) and the Average Nearest Neighbor Ratio (R) was less than 1 (R = 0.408, P < 0.001) (Table 3), which indicates a clustered pattern of red-bellied monkey suitable habitats in the landscape and was confirmed by a pair correlation function g(r) (Fig. 6).

Fig. 5.

Fig. 5.

Open in a new tab

Size distribution of suitable habitats in the study area in southern Benin. The number above each bar represents the percentage of the suitable habitat size class considered.

Table 3.

Parameters calculated for suitable habitats identified

graphic file with name zoolstud-61-047-t003.jpg

Open in a new tab

Table 4.

Size distribution of suitable habitats

graphic file with name zoolstud-61-047-t004.jpg

Open in a new tab

Fig. 6.

Fig. 6.

Open in a new tab

Pair correlation function g(r) of suitable habitats. Where the observed gRipley (black line) or gTranslate (red dashed line) is greater than gPois (green dashed line) we can expect more clustering than expected and where the observed gRipley or gTranslate is less than gPois we can expect more dispersion than expected.

DISCUSSION

Data on habitat requirements for the red-bellied monkey are needed for effective management and conservation. We used empirical models—namely maximum entropy—to identify suitable habitats for the red-bellied monkey and highlight important areas for the species habitats conservation. This is the first study to use landscape characteristics with habitat requirements to model habitat suitable for the red-bellied monkey. The model was generated in the study area in southern Benin beyond the limits of the prospected forests, which could have an impact on the results and therefore requires careful interpretation. According to Eger et al. (2016), transferring a model can cause the response curves to be truncated, and reduce the model’s predictive ability in the new region. However, our results show that the final model perfectly describes the red-bellied monkey suitable habitats (AUC = 0.974 ± 0.034). Models with AUC values greater than 0.90 are considered to have high discriminative abilities and are ecologically useful (Araujo et al. 2005). The variables that contributed the most to predicted habitat suitability were: Distance to Water (47.7%), Land Cover Class (23.1%), Brightness (17.0%), Wetness (4.7%), Population Size (2.8%) and Elevation (2.2%).

Our finding that red-bellied monkey presence had a high probability in areas with low Distance to Water suggests that the red-bellied monkey prefers places close to water. Indeed, the red-bellied monkey is a species that does not move away from rivers and finds its preferential habitat in gallery forests and swamp forests (Kassa et al. 2007; Nobimè et al. 2009).

Land Cover Class was the next most important variable included in the model. The Land Cover Class that induced the greatest probability of red-bellied monkey presence is Dense Forest (0.68), followed by Mixed Forest (0.08) and Farms and Fallow (0.01). Nobimè and Sinsin (2007) obtained similar results in the Lama Forest Reserve, where the lowest observation was obtained in fallows with less than 5% sighting and the highest observations in dense forests followed by mixed forests. The dense forest presents closed and photosynthetically active vegetation and is a place where the red-bellied monkey finds a fairly closed undergrowth surmounted by large trees to nest and hide (Kassa et al. 2007; Nobimè and Sinsin 2007). Furthermore, the dense forest represents the place where we find the preferential food resources of the red-bellied monkey, namely Dialium guineense, Diospyros mespiliformis, Mimusops andongensis, Ficus sp. and others (Kassa et al. 2007; Zoffoun et al. 2018). This indicates that the habitat suitability model is in fact modelling the red-bellied monkey use of the landscape.

Brightness was another important variable. A low Brightness value (0.28–0.37) induced a high probability of presence. Brightness increase it indicates an increase in the open canopy and an increase in the bare ground (Cohen et al. 1995; Campbell and Wynne 2011). Cohen et al. (1995) showed that closed forest stands tend to have moderate brightness values. The red-bellied monkey prefers dense forests with dense undergrowth (Kassa et al. 2007; Nobimè and Sinsin 2007); this demonstrates why the presence of the red-bellied monkey is high in areas with low Brightness (0.28–0.37). Thus, the results from this study, showing the highest probability of presence at moderate brightness values, support previous findings.

Habitat suitability is also affected by Wetness. There is a strong positive correlation between the probability of presence and Wetness. Indeed, positive great wetness value (0.02–0.1) induced a high probability of presence. The areas where people previously reported red-bellied monkey presence were characterized by humid habitats that maintained high humidity throughout the year: swamp forests, gallery forests and dense semi-deciduous forests, which flood during the rainy season (Nobimè et al. 2009).

Population Size also determined the probability of red-bellied monkey presence. Population Size and probability of presence is inversely proportional. When the number of people in each 900 m2 grid is close to 0 the probability of presence is high (between 0.5 and 0.9). Indeed, in the red-bellied monkey habitat, there is little human activity for the three months or more per year when the area is flooded, providing, therefore, a good refuge (Sinsin et al. 2002; Agbessi et al. 2017). According to Agbessi et al. (2017), the red-bellied monkey prefers remote areas that are difficult for humans to access.

Elevation was the last important variable including in the red-bellied suitable habitat model. The probability of presence is high for a positive low elevation value between 40 and 100 m. This topographic situation marked by a low elevation allows water to remain on the surface periodically and humidity to remain high throughout the year; it also allows for good vegetation with high canopy cover like suggested by Sinsin et al. (2002) while describing red-bellied monkey suitable habitats condition.

The binary output of the final model of habitat suitability classified 3.62% of the landscape as suitable and 96.38% was unsuitable for the red-bellied monkey. The final model illustrates the high concentration of habitat suitability for the red-bellied monkey in the protected area (57.46%) and mainly in the Central Core of the Lama Forest Reserve (49.5%). Protected areas play a special role in the conservation of wildlife habitats (Naughton-Treves et al. 2005) because they offer shelter when unprotected natural habitats are detroyed. The proportion of suitable habitat in the study area landscape is low due to the presence of highly urbanized areas in the landscape. The majority of areas suitable for the red-bellied monkey found in protected areas offer an opportunity for the sustainable conservation of the red-bellied monkey if measures are taken to ensure good management of these forests.

The landscape is fragmented and 91.49% of suitable habitats are 0 to 0.01 km2 in size with 0.017 ± 0.545 km2 mean size of suitable habitats in the landscape. The fragmentation of the landscape is a factor that changes the density, composition, and size of groups (Clarke et al. 2002). It isolates sub-populations, which can lead them to extinction due to excessive interbreeding when the disruption of the original patterns of gene flow, drift-induced differentiation among local populations occurs during long periods (Yumnam et al. 2014). Furthermore, small and isolated populations also are more likely to become extinct by stochastic events such as diseases, climate change, or natural disasters (Colchero et al. 2009). Habitat fragmentation is particularly relevant in developing countries, where most of the terrestrial biodiversity occurs. Natural ecosystems in developing countries are under unprecedented threats due to excessive population growth, demand by human populations for new lands for agriculture, and unplanned economic development (Tapia-Armijos et al. 2015). The fragmentation of the landscape is, therefore, a very important factor to take into account in our landscape for the development of conservation plans because urbanization is rampant and the last areas suitable for the red-bellied monkey could be occupied by people in the next years. This fragmentation of red-bellied monkey suitable habitats in southern Benin was previously shown by Sinsin et al. (2000) and continues more and more until today due to intensive logging, shifting cultivation, wildland fire, excessive cutting of firewood to commercial and domestic purposes, pasture creation and clearing for crop establishment. However, there is still hope because the average distance between the red-bellied monkey suitable habitats in southern Benin is very low (around 100 m) with clustered pattern of suitable habitats. Also, the red-bellied monkey is known in Togbota landscape for its ability to move from suitable habitats that are often small in size and able to cross thickets and fallows and go into farms to feed and thus exposes itself to reprisals from farmers (Zoffoun et al. 2019). This makes it possible to foresee the establishment of a network of connections between the red-bellied monkey suitable habitats. One of the main solutions for mending the negative effects of habitat fragmentation on wildlife populations is to maintain or restore connectivity through wildlife corridors (Wang et al. 2014).

Our study used little occurence data to define the red bellied monkey suitable habitat. Use of more occurence data could define more precise suitable habitat for the subspecies. Additionally, our model was projected into areas in the study area where the occurrences data were not collected, this can create a bias in the response curve that may not encompass the full range of variables. This is a limitation of predictive Species Distribution Model (SDM) studies, and there are few solution (Elith et al. 2010; Eger et al. 2016). Future research should survey more areas to make the sampling effort more representative of the range of the predictor variables as well as use higher spatial resolution variables in order to better capture vegetation characteristics.

CONCLUSIONS

The biophysical variables used to predict the red-bellied monkey presence in the study area in southern Benin has been shown to be good predictors. Thus, the precursor variables that contributed the most to the model were: Distance to Water (47.7%), Land Cover Class (23.1%), Brightness (17.0%), Wetness (4.7%), Human Population Size (2.8%) and Elevation (2.2%). The suitable habitat model showed that the majority of suitable habitats in the study area are in protected areas, mainly the Lama Forest Reserve. However, this forest is under the influence of a growing threat of hunting. It is therefore important to ensure better protection of this protected area for the sustainable conservation of the red-bellied monkey population. Besides, the suitable habitats identified in the study area are of great importance and conservation effort can be focused in this area for the red-bellied monkey sustainable conservation. The suitable habitats are fragmented but with Low Average Nearest Neighbor Distance and Ratio, which indicates cluster pattern of the suitable habitats. This allows us to glimpse connections between these isolated suitable habitats in order to preserve genetic diversity within metapopulations, and thus their resistance to different environmental variations, by promoting gene flow. Future studies can use suitable habitat models and develop connectivity models to link suitable habitats where restoration and reforestation efforts can be focused for the long-term survival of red-bellied monkey populations.

Supplementary materials

Table S1.

Red-bellied monkey sighting GPS locations (visual contact) used for modelling.

zoolstud-61-047-s001.docx (15.6KB, docx)

Acknowledgments

Our thanks to the IDEA WILD, for materials support, which allowed us to carry out this study. We extend our appreciation to the field guides of the communities of Togbota, Lokoli, Gnanhouizounmè and Massi for their efforts and sacrifices during field activities. I also thank the reviewers and the Associate Editor who contributed to improving the scientific quality of this paper.

Footnotes

Authors’ contributions: OGZ contributed with data collection, materials/analysis tools, wrote the paper, prepared figures and tables, and reviewed drafts of the paper; CAL contributed with materials/analysis tools, wrote the paper, and reviewed drafts of the paper; GN wrote the paper and reviewed drafts of the paper; and BS conceived and designed the data collection and analysis as well as reviewed drafts of the paper.

Competing interests: OGZ, CAL, GN and BS declare that they have no conflict of interest.

Availability of data and materials: Geographical records in UTM of red-bellied monkey occurrence are available in supplemental table S1.

Consent for publication: All authors have read and approved the submission of this manuscript to Zoological Studies.

Ethics approval consent to participate: Not applicable.

References

  1. Agbessi KGE, Ouedraogo M, Camara M, Segniagbeto H, Houngbedji MB, Kabre AT. 2017. Distribution spatiale du singe à ventre rouge, Cercopithecus erythrogaster erythrogaster Gray et les menaces pesant sur sa conservation durable au Togo. Int J Biol Chem Sci 11(1):157–173. doi:10.4314/ijbcs.v11i1.13.
  2. Ali SM, Salman SS. 2016. New Tasseled Cap Classification Technique using Landsat-8 OLI Image Bands. Iraqi J Sci 57(2C):1612–1619.
  3. Araujo MB, Pearson RG, Thuillers W, Erhard M. 2005. Validation of species–climate impact models under climate change. Glob Change Biol 11:1504–1513. doi:10.1111/j.1365-2486.2005. 01000.x.
  4. Badiella-Giménez N, Kankam BO, Badiella L. 2021. Influence of visitors on the time budget, ranging and strata use of Lowe’s Monkey (Cercopithecus lowei) at Boabeng-Fiema Monkey Sanctuary, Ghana. Zool Stud 60:51. doi:10.6620/ZS.2021.60-51. . [DOI] [PMC free article] [PubMed]
  5. Ban SD, Boesch C, Janmaat KRL. 2014. Taï chimpanzees anticipate revisiting high-valued fruit trees from further distances. Anim Cogn 17:1353–1364. doi:10.1007/s10071-014-0771-y. . [DOI] [PubMed]
  6. Bergl RA, Brenda JB, Nsubuga A, Vigilant L. 2008. Effects of habitat fragmentation, population size and demographic history on genetic diversity: the cross-river gorilla in a comparative context. Am J Primatol 70:848–859. doi:10.1002/ajp.20559. . [DOI] [PubMed]
  7. Campbell G, Teichroeb J, Paterson JD. 2008. Distribution of diurnal primates species in Togo and Benin. Folia Primatol 79(1):15–30. doi:10.1159/000108383. . [DOI] [PubMed]
  8. Campbell JB, Wynne RH. 2011. Introduction to Remote Sensing. Fifth ed. The Guilford Press, New York. doi:10.1111/phor.12021.
  9. Clarke MR, Crockett CM, Zucker EL, Zaldivar M. 2002. Mantled howler population of Hacienda La Pacifica, Costa Rica, between 1991 and 1998. Effects of deforestation. Am J Primatol 56:155–163. doi:10.1002/ajp.1071. [DOI] [PubMed]
  10. Cohen WB, Spies TA, Fiorella M. 1995. Estimating the age and structure of forests in a multi-ownership landscape of western Oregon, U.S.A. Int J Remote Sens 16:721–746. doi:10.1080/01431169508954436.
  11. Colchero F, Medellin RA, Clark JS, Lee R, Katul GG. 2009. Predicting population survival under future climate change: density dependence, drought and extraction in an insular bighorn sheep. J Anim Ecol 78:666–673. doi:10.1111/j.1365-2656.2009.01528.x. . [DOI] [PubMed]
  12. Congedo L, Munafò M. 2012. Development of a methodology for land cover classification in Dar es Salaam using Landsat imagery. Sapienza University, Rome. Working paper.
  13. Eger AM, Curtis JM, Fortin MJ, Côté IM, Guichard F. 2016. Transferability and scalability of species distribution models: A test with sedentary marine invertebrates. Can J Fish Aquat Sci 74:766–778. doi:10.1139/cjfas-2016-0129.
  14. Elith J, Kearney M, Phillips S. 2010. The art of modelling range-shifting species. Methods Ecol Evol 1:330–342. doi:10.1111/j.2041-210X.2010.00036.x.
  15. Elith J, Phillips SJ, Hastie SJ, Dudík M, Chee YE, Yates CJ. 2011. A statistical explanation of MaxEnt for ecologists. Divers Distrib 17:43–57. doi:10.1111/j.1472-4642.2010.00725.x.
  16. Estes LD, Reillo PR, Mwangi AG, Okin GS, Shugart HH. 2010. Remote sensing of structural complexity indices for habitat and species distribution modeling. Remote Sens Environ 114(4):792–804. doi:10.1016/j.rse.2009.11.016.
  17. Estrada A, Garber AP, Rylands AB, Roos C, Fernandez-Duque E, Di Fiore A, Nekaris KAI, Nijman V, Heymann EW, Lambert JE, Rovero F, Barelli C, Setchell JM, Gillespie TR, Mittermeier RA, Arregoitia LV, De Guinea M, Gouveia S, Dobrovolski R, Shanee S, Shanee N, Boyle SA, Fuentes A, MacKinnon KC, Amato KR, Meyer ALS, Wich S, Sussman RW, Pan R, Kone I, Li B. 2017. Impending extinction crisis of the world’s primates: Why primates matter. Sci Adv 3:e1600946. doi:10.1126/sciadv.1600946. . [DOI] [PMC free article] [PubMed]
  18. Evans JS, Oakleaf J, Cushman SA, Theobald D. 2014. An ArcGIS Toolbox for Surface Gradient and Geomorphometric Modeling, version 2.0-0. GIS software. Available at: http://evansmurphy. wix.com/evansspatial.
  19. Fitzgerald M, Coulson R, Lawing AM, Matsuzawa T, Koops K. 2018. Modeling habitat suitability for chimpanzees (Pan troglodytes verus) in the Greater Nimba Landscape, Guinea, West Africa. Primates 59(4):361–375. doi:10.1007/s10329-018-0657-8. . [DOI] [PubMed]
  20. Goodwin MR, Oates JF, Segniagbeto GH, Nobimè G, Mittermeier RA. 2020. Cercopithecus erythrogaster ssp. erythrogaster. The IUCN Red List of Threatened Species 2020:e.T40003A17984113. doi:10.2305/IUCN.UK.2020-2.RLTS.T40003A17984113.en. Accessed 11 Jan. 2022.
  21. Guisan A, Zimmerman NE. 2000. Predictive habitat distribution models in ecology. Ecol Model 135:147–186. doi:10.1016/S0304-3800(00)00354-9.
  22. Hernandez PA, Graham CH, Master LL, Albert DL. 2006. The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29:773–785. doi:10.1111/j.0906-7590.2006.04700.x.
  23. Kassa B, Nobimè G, Hanon L, Assogbadjo A, Sinsin B. 2007. Caractéristiques de l’habitat du singe à ventre rouge (Cercopithecus e. erythrogaster) dans le Sud-Bénin. In: Fournier A, Sinsin B, Mensah GA (ed) Actes du Séminaire International sur l’aménagement et la gestion des aires protégées de l’Afrique de l’Ouest. Quelles aires protégées pour l’Afrique de l’Ouest? Paris, 2007. fdi:010044699.
  24. Kumar S, Neven LG, Yee WL. 2014. Evaluating correlative and mechanistic niche models for assessing the risk of pest establishment. Ecosphere 5:86. doi:10.1890/ES14-00050.1.
  25. Liu C, White M, Newell G. 2013. Selecting thresholds for the prediction of species occurrence with presence-only data. J Biogeogr 40(4):778–789. doi:10.1111/jbi.12058.
  26. McCune B, Dylan K. 2002. Equations for potential annual direct incident radiation and heat load. J Veg Sci 13:603–606. doi:10.1111/j.1654-1103.2002.tb02087.x.
  27. Moore DA, Carpenter TE. 1999. Spatial analytical methods and Geographic Information System: Use in health research and epidemiology. Epidemiol Rev 21:143–161. doi:10.1093/oxfordjournals.epirev.a017993. . [DOI] [PubMed]
  28. Moore ID, Gessler PE, Nielsen GA, Petersen GA. 1993. Terrain attributes: estimation methods and scale effects. In: Jakeman AJ, Beck MB, McAleer Wiley M (ed) Modeling Change in Environmental Systems. John Wiley and Sons Ltd, London, pp. 189–214.
  29. NASA/METI/AIST/Japan Space systems, U.S./Japan ASTER Science Team. 2018. ASTER Global Digital Elevation Model V003. Distributed by NASA EOSDIS Land Processes DAAC.
  30. Naughton-Treves L, Maggie H, Katrina B. 2005. The Role of Protected Areas in Conserving Biodiversity and Sustaining Local Livelihoods. Annu Rev Environ Resour 17:219–252. doi:10.1146/annurev.energy.30.050504.164507.
  31. Nobimè G. 2012. Facteur écologiques et éthologiques déterminants pour la conservation du singe à ventre rouge « Zinkaka » Cercopithecus erythrogaster erythrogaster dans des habitats du Sud du Bénin. Thèse de Doctorat, Université d’Abomey-Calavi, Abomey-Calavi.
  32. Nobimè G, Sinsin B. 2007. Diversité des primates dans la forêt classée de la Lama (Bénin). In: Fournier A, Sinsin B, Mensah GA (ed) Actes du Séminaire International sur l’aménagement et la gestion des aires protégées de l’Afrique de l’Ouest. Quelles aires protégées pour l’Afrique de l’Ouest? Paris, 2007.
  33. Nobimè G, Sinsin B, Lernould J-L. 2009. Ecological factors determining the distribution of the red-bellied guenon Cercopithecus e. erythrogaster in Benin and Togo. Int J Biol Chem Sci 3(3):606–611. doi:10.4314/ijbcs.v3i3.45326.
  34. Nobimè G, Assogbadjo EA, Sinsin B. 2010. Les primates. In: Sinsin B, Kampmann D (ed) Atlas de la biodiversité de l’Afrique de l’Ouest. Cotonou and Frankfurt/Main, pp. 470–473.
  35. ONAB. 2005. Amenagement Participatif des Plantations Forestieres D’agrimey, Akpe, Djigbe, Koto, Massi Et Toffo: Période 2004-2023. Office National du Bois (ONAB), Bénin.
  36. ONAB. 2011. Plan Directeur d’Aménagement et de Gestion du Noyau Central de la Forêt Classée de la Lama 2011-2020. Office National du Bois (ONAB), Bénin.
  37. Papes M, Gaubert P. 2007. Modelling ecological niches from low numbers of occurrences: Assessment of the conservation status of poorly known viverrids (Mammalia, Carnivora) across two continents. Divers Distrib 13(6):890–902. doi:10.1111/j.1472-4642.2007.00392.x.
  38. Pearson RG, Raxworthy CJ, Nakamura M, Townsend Peterson A. 2007. Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J Biogeogr 34:102–117. doi:10.1111/j.1365-2699. 2006.01594.x.
  39. Pebesma EJ, Bivand RS. 2005. Classes and methods for spatial data in R. R News 5(2):9–13.
  40. Peres CA. 1999. General guidelines for standardizing line transect surveys of tropical forest primates. Neotrop Primates 7:11–16.
  41. Phillips SJ, Anderson RP, Schapire RE. 2006. Maximum entropy modeling of species geographic distributions. Ecol Model 190:231–259. doi:10.1016/j.ecolmodel.2005.03.026.
  42. Phillips SJ, Dudik M. 2008. Modeling of species distributions with MaxEnt: new extensions and a comprehensive evaluation. Ecography 3:161–175. doi:10.1111/j.0906-7590.2008.5203.x.
  43. Phillips SJ, Dudík M, Elith J, Graham CH, Lehmann A, Leathwick J, Ferrier S. 2009. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecol Appl 19:181–197. doi:10.1890/07-2153.1. . [DOI] [PubMed]
  44. Phillips SJ, Anderson RP, Dudik M, Schapire RE, Blair ME. 2017. Opening the black box: an open-source release of Maxent. Ecography 40(7):887–893. doi:10.1111/ecog.03049.
  45. Polasky S, Camm JD, Solow AR, Csuti B, White D, Ding R. 2000. Choosing reserve networks with incomplete species information. Biol Conserv 94:1–10. doi:10.1016/S0006-3207(99)00171-8.
  46. R Development Core Team. 2019. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.
  47. Rodrigues ASL, Andelman SJ, Bakarr MI, Boitani L, Brooks TM, Cowling RM, Fishpool LDC, Fonseca GAB, Gaston KJ, Hoffman M, Long J, Marquet PA, Pilgrim JD, Pressey RL, Schipper J, Sechrest W, Stuart SN, Underhill LG, Waller RW, Watts MEJ, Xie Y. 2003. Global Gap Analysis: towards a representative network of protected areas. Advances in Applied Biodiversity Science 5. Conservation International, Washington DC, USA.
  48. Serckx A, Huynen MC, Beudels-Jamar RC, Vimond M, Bogaert J, Kühl HS. 2016. Bonobo nest site selection and the importance of predictor scales in primate ecology. Am J Primatol 78:1326–1343. doi:10.1002/ajp.22585. . [DOI] [PubMed]
  49. Sinsin B, Tehou AC, Nobimè G, Tchibozo S. 2000. Répartition et abondance du singe à ventre rouge Cercopithecus erythrogaster dans les régions de la Lama et d’Adjohoun (Bas-Bénin). LEA/FSA/UNB, Cotonou, Bénin.
  50. Sinsin B, Nobimè G, Téhou A, Bekhuis P, Tchibozo S. 2002. Past and present distribution of red-bellied monkey Cercopithecus erythrogaster erythrogaster in Benin. Folia Primatol 73:116–123. doi:10.1159/000064789. . [DOI] [PubMed]
  51. Solano CE, Feria ATP. 2007. Ecological niche modeling and geographic distribution of the genus Polianthes L. (Agavaceae) in Mexico: using niche modeling to improve assessments of risk status. Biodivers Conserv 16:1885–1900. doi:10.1007/s10531-006-9091-0.
  52. Tapia-Armijos MF, Homeier J, Espinosa CI, Leuschner C, de la Cruz M. 2015. Deforestation and forest fragmentation in South Ecuador since the 1970s –Losing a hotspot of biodiversity. PLoS ONE 10:e0133701. doi:10.1371/journal.pone.0133701. . [DOI] [PMC free article] [PubMed]
  53. UNEP-WCMC. 2014. Checklist of CITES species. CITES Secretariat, Geneva, Switzerland, and UNEP-WCMC, Cambridge, United Kingdom.
  54. Wang F, McShea WJ, Wang D, Li S, Zhao Q, Wang H, Lu Z. 2014. Evaluating landscape options for corridor restoration between Giant Panda reserves. PLoS ONE 9:e105086. doi:10.1371/journal.pone.0105086. . [DOI] [PMC free article] [PubMed]
  55. Wilson KA, Westphal MI, Possingham HP, Elith J. 2005. Sensitivity of conservation planning to different approaches to using predicted species distribution data. Biol Conserv 122:99–112. doi:10.1016/j.biocon.2004.07.004.
  56. Wilson JW, Sexton JO, Todd Jobe R, Haddad NM. 2013. The relative contribution of terrain, land cover, and vegetation structure indices to species distribution models. Biol Conserv 164:170–176. doi:10.1016/j.biocon.2013.04.021.
  57. Yumnam B, Jhala YV, Qureshi Q, Maldonado JE, Gopal R, Saini S, Srinivas Y, Fleischer RC. 2014. Prioritizing tiger conservation through landscape genetics and habitat linkages. PLoS ONE 9:e111207. doi:10.1371/journal.pone.0111207. . [DOI] [PMC free article] [PubMed]
  58. Zoffoun OG, Nobimè G, Adjahossou S, Djego J. 2018. Diversité floristique des ressources alimentaires du singe à ventre rouge (Cercophithecus erythrogaster erythrogaster) dans les formations végétales de Togbota au Sud-Bénin. In: Centre Béninois de la Recherche Scientifique et Technique (ed) Actes de la Semaine scientifique de la Journée de la renaissance scientifique de l’Afrique (JRSA), Cotonou, 2017.
  59. Zoffoun OG, Nobimè G, Adjahossou S, Djego J. 2019. Déprédation des cultures par le singe à ventre rouge (Cercophithecus erythrogaster erythrogaster) à Togbota au Sud-Bénin. Afr Primates 13:9–28.

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1.

Red-bellied monkey sighting GPS locations (visual contact) used for modelling.

zoolstud-61-047-s001.docx (15.6KB, docx)

Articles from Zoological Studies are provided here courtesy of Biodiversity Research Center, Academia Sinica

RESOURCES