Biome transitions as centres of diversity: habitat heterogeneity and diversity patterns of West African bat assemblages across spatial scales
2010; Wiley; Volume: 34; Issue: 2 Linguagem: Inglês
10.1111/j.1600-0587.2010.05510.x
ISSN1600-0587
AutoresJakob Fahr, Elisabeth K. V. Kalko,
Tópico(s)Wildlife Ecology and Conservation
ResumoEcographyVolume 34, Issue 2 p. 177-195 Open Access Biome transitions as centres of diversity: habitat heterogeneity and diversity patterns of West African bat assemblages across spatial scales Jakob Fahr, Jakob FahrSearch for more papers by this authorElisabeth K. V. Kalko, Elisabeth K. V. KalkoSearch for more papers by this author Jakob Fahr, Jakob FahrSearch for more papers by this authorElisabeth K. V. Kalko, Elisabeth K. V. KalkoSearch for more papers by this author First published: 13 August 2010 https://doi.org/10.1111/j.1600-0587.2010.05510.xCitations: 62 J. Fahr (jakob.fahr@gmail.com), Inst. of Experimental Ecology, Ulm Univ., DE-89069 Ulm, Germany. – E. K. V. Kalko, Inst. of Experimental Ecology, Ulm Univ., DE-89069 Ulm, Germany, and Smithsonian Tropical Research Inst., Apartado Postal 0843-03092, Balboa, Panama. AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract It is widely accepted that species diversity is contingent upon the spatial scale used to analyze patterns and processes. Recent studies using coarse sampling grains over large extents have contributed much to our understanding of factors driving global diversity patterns. This advance is largely unmatched on the level of local to landscape scales despite being critical for our understanding of functional relationships across spatial scales. In our study on West African bat assemblages we employed a spatially explicit and nested design covering local to regional scales. Specifically, we analyzed diversity patterns in two contrasting, largely undisturbed landscapes, comprising a rainforest area and a forest-savanna mosaic in Ivory Coast, West Africa. We employed additive partitioning, rarefaction, and species richness estimation to show that bat diversity increased significantly with habitat heterogeneity on the landscape scale through the effects of beta diversity. Within the extent of our study areas, habitat type rather than geographic distance explained assemblage composition across spatial scales. Null models showed structure of functional groups to be partly filtered on local scales through the effects of vegetation density while on the landscape scale both assemblages represented random draws from regional species pools. We present a mixture model that combines the effects of habitat heterogeneity and complexity on species richness along a biome transect, predicting a unimodal rather than a monotonic relationship with environmental variables related to water. The bat assemblages of our study by far exceed previous figures of species richness in Africa, and refute the notion of low species richness of Afrotropical bat assemblages, which appears to be based largely on sampling biases. Biome transitions should receive increased attention in conservation strategies aiming at the maintenance of ecological and evolutionary processes. Quantifying and explaining the spatial distribution of life on Earth is a central focus of contemporary ecological research. In most taxa, species richness increases from the poles towards the equator (Hillebrand 2004). Since standardized data collection has been rarely achieved over broad spatial extents, many studies analyzed drivers of species richness using large sampling units such as gridded range maps or point records generalized to larger areas (Lyons and Willig 1999, 2002, Ceballos and Ehrlich 2006, Orme et al. 2006, Davies et al. 2007). Accordingly, these studies focused on the regional scale as their underlying data do not account for lacunarity or range porosity, that is an increasing loss of species with increasing spatial resolution (Hurlbert and White 2005, Hurlbert and Jetz 2007). The causative mechanisms driving species richness are still hotly debated, and some of the conflicting results might be explained by the scale-dependency of species richness (Rahbek 2005). A major conceptual advancement has been the recognition that local and regional processes act in concert to result in a community or, more neutrally defined, a point estimate of overlapping regional species distributions (Ricklefs 2004). At regional scales, speciation, extinction, and immigration create, over evolutionary time, regional species pools. At local scales, habitat selection and species interactions as well as stochastic processes may be important. To predict species richness in relation to environmental conditions requires an understanding of the relative contribution of these processes along a spatially nested hierarchy (Ricklefs 1987, 2004, Cornell and Lawton 1992, Loreau 2000). The landscape scale connects local and regional scales and thus is of immense interest for studying patterns and causes of species richness (Böhning-Gaese 1997, Whittaker et al. 2001). Within ecological time, regional diversity sets the limit for species richness at the local scale. To identify processes that determine local diversity, we need to ask how beta diversity, or species turnover, links regional and local scales. In Whittaker's (1960) multiplicative approach, regional diversity (in his terms gamma diversity) is the product of beta diversity and local (or alpha) diversity. However, this approach does not allow direct comparison of the relative contribution of these factors because regional and local diversities are measured as the number of species (or related units that incorporate the abundance of species), while beta diversity is dimensionless. Alternatively, diversities can be partitioned additively where regional diversity=local diversity+beta diversity (Lande 1996, Loreau 2000, Veech et al. 2002). This additive approach defines beta diversity as species turnover and therefore is well suited to analyze the relative contributions of diversity components across spatial scales (Wagner et al. 2000, Crist et al. 2003, Summerville et al. 2003, Freestone and Inouye 2006, Veech and Crist 2007). Habitat heterogeneity is considered an important mechanistic factor driving species richness: only few species are found in all habitats, hence an increase in habitat types should lead to more species when sampled across a landscape (Rosenzweig 1995, Kerr et al. 2001, Tews et al. 2004). Several studies assessed this relationship across large spatial extents and employed variables such as altitudinal range or number of land cover classes per grid cell as proxies for habitat heterogeneity (Kerr and Packer 1997, Rahbek and Graves 2001, Van Rensburg et al. 2002, Ruggiero and Kitzberger 2004). Since these studies employed relatively coarse grain, habitat heterogeneity might have been missed as an explanatory variable of species diversity because ecologically relevant heterogeneity is likely to be perceived by organisms at finer grain depending on factors such as body size and dispersal ability. We assessed diversity and assemblage structure of bats (Chiroptera) in two largely undisturbed areas in Ivory Coast, West Africa, and asked which factors drive species diversity from local to regional scales. We employed a spatially explicit and nested design that ranged from local to regional scales to account for the influence of spatial grain and extent (Wiens 1989, Whittaker et al. 2001, Ricklefs 2004, Rahbek 2005). We analyzed constant sample units (plots) that were hierarchically grouped within landscapes, hence keeping the sample grain invariant while changing the sample "focus" or area of inference (Scheiner et al. 2000). The extent of the landscape scale was chosen to match the dispersal abilities of our study group. As bats show a broad suite of habitat-related adaptations, most notably in their sensory systems (echolocation, passive listening, vision, and smell) and morphology (wing shape) (Norberg and Rayner 1987, Neuweiler 1989, Schnitzler and Kalko 2001, Safi and Dechmann 2005), we hypothesized that species richness of bats should be positively related to environmental heterogeneity as heterogeneous habitats should offer more niches than uniform ones. We differentiated between habitat complexity and habitat heterogeneity (August 1983), where heterogeneity is defined as the horizontal variability or patchiness of a habitat and complexity refers to the development of vertical strata within a habitat. In our approach, heterogeneity of vegetation types is taken as the most relevant habitat parameter for the majority of bat species as well as the most commonly used variable in previous studies (Tews et al. 2004). Our study was conducted in two contrasting landscapes along the steep climatic gradient of West Africa, which is characterized by the staggered arrangement of biomes that stretch from the rainforest zone in the south through various savanna types up to the Sahara Desert in the north. Variables such as annual precipitation, actual evapotranspiration, and net primary productivity decrease along this S-N-gradient while seasonality increases (Tateishi and Ahn 1996, Imhoff et al. 2004). If water-related variables best explain broad-scale patterns of species richness of animals in the (sub)tropics (Hawkins et al. 2003), species richness of bats should monotonically increase from deserts to forests. If habitat heterogeneity drives species richness, one would expect a unimodal gradient with a peak at intermediate latitudes corresponding to the structurally most heterogeneous region along the biome transition ("Guinea Zone") between forests and savannas (Goetze et al. 2006). We hypothesized first that species diversity increases with habitat heterogeneity through the effects of beta diversity. Second, we postulated a positive relation between habitat complexity and species diversity. Third, habitat type rather than geographic distance should explain diversity patterns across spatial scales. Fourth, we expected that the structure of functional groups within a habitat type is not a random draw from the combined landscape assemblage but a selectively filtered subset. Fifth, we hypothesized that the reputed impoverishment of Afrotropical bat assemblages (Findley 1993) is largely due to sampling biases. Material and methods Study sites We assessed bat assemblages in two areas in Ivory Coast, West Africa: Taï National Park (TNP) and Comoé National Park (CNP), which are ca 500 km apart. TNP (4550 km2) is located in southwestern Ivory Coast and constitutes the largest protected rainforest in West Africa in conjunction with the adjacent "Réserve de faune du N′Zo" (790 km2). The study was carried out in the vicinity of the "Centre de Recherche en Ecologie" station (CRE; 5°50′N, 7°21′W). The rolling landscape (ca 200 m a.s.l.) consists of a mosaic of drier and wetter parts. The climate is subequatorial seasonal, with an annual precipitation of 1813±268 mm in the study area (1978–1982, 1988–2002; Taï Monkey Project unpubl.) and two dry seasons: a minor one in July–August and a major one from December to February. Floristically, TNP belongs to the "Guineo-Congolian regional centre of endemism" (White 1983) and the "Western Guinean lowland forests" ecoregion (Olson et al. 2001). Our study area was composed of a mosaic of evergreen forest on the lower slopes with patches of deciduous trees on hill tops (Van Rompaey 1993). Apart from the clearing around the research station, treefall gaps and a few sparsely vegetated inselbergs, the study area is covered by a closed canopy. In TNP, we differentiated between two major forest types according to their physical structure: hill forest ("forêt sèche") on slopes and hill tops vs swamp forest ("bas-fond") on the bottom of seasonally flooded valleys. Hill forest was characterized by high stature of mature trees and a comparatively open understorey (shrub and herb layer). Swamp forest had a higher density of smaller trees and a denser understorey, mainly composed of Raphia palms and Marantaceae. We established six plots arranged in three pairs, where one plot of each pair represented hill forest and the other swamp forest. Distances between plots were 0.2–2.8 km (median: 2.1 km), with distances between paired plots of 0.2–0.3 km, and distances between plot pairs of 1.0–2.6 km. Despite the short distance between paired plots, mark-recapture data showed that very few bats crossed from one plot to its neighbouring pair (19 out of 844 marked bats [2.3%], and 17.6% of all recaptures [108 individuals]), while recapture rate within plots was high (83 recaptured bats [9.8%], and 76.9% of all recaptures), thus justifying to treat each of the paired plots as an independent sample. CNP (11 493 km2) is located in northeastern Ivory Coast and represents the largest protected area in the savanna zone of West Africa. The study was conducted around the former research station of the Univ. of Würzburg (Lola Camp: 8°45′N, 3°49′W, ca 200 m a.s.l.). The landscape generally is flat but occasionally broken by inselbergs or low rocky outcrops. The climate is of humid Sudanian type, with an annual precipitation of 1003±173 mm in the study area (1993–2002; Univ. of Würzburg unpubl.), a single dry season from November to March, and a wet season from April to October. Floristically, the southern portion of CNP belongs to the transition zone between the "Sudanian woodland with abundant Isoberlinia" and the "Mosaic of lowland rainforest and secondary grassland" (White 1983), which is part of the "Guinean forest-savanna mosaic" ecoregion (Olson et al. 2001). The study region is characterized by a matrix of bush-tree savanna with embedded patches of semi-deciduous forest islands ranging in size from >1 ha to several km2. Extensive gallery forests with evergreen elements occur along the main rivers Comoé, Iringou, and Kongo. The wider stretches of gallery forest and some larger forest islands structurally resemble rainforest and show floristic affinities to Guineo-Congolian lowland forests (Hovestadt et al. 1999). The three main habitat types (savannas: covering 84.2% of the area; forest islands: 8.4%; gallery forests: 2.3%) result in an overall mosaic-like landscape structure with clearly defined edges between vegetation types (Hovestadt et al. 1999, Hennenberg et al. 2005, Goetze et al. 2006). In CNP, we sampled bat assemblages in three vegetation formations: open bush-tree savanna ("savanes boisée" and "savanes arbustive"), forest islands, and gallery forest. Initially, we established two plots in each of these three habitat types. A third savanna plot was added during the second half of the study period, resulting in a total of seven plots. Distances between plots ranged between 1.4 and 13.9 km (median: 5.4 km). The large distances compared to TNP result from one plot in gallery forest that was situated rather far from the other plots. Sampling design Each plot comprised 12 mist nets arranged in a standardized configuration along a 200×100 m-rectangle (2 ha), with equidistant (50 m) centres of the nets. The nets were oriented in an alternating fashion perpendicular to one another. These "understorey nets" (UN) were set on poles near ground level or slightly elevated, with the lower net edge level with the surrounding soil or herb layer. In addition, we set up one elevated net system in each plot, which consisted of a pulley and rope structure to hoist four stacked nets usually reaching a height of ca 25 m. These "canopy nets" (CN) were established within, or close to, the rectangle formed by the understorey nets, in TNP within natural treefall gaps, in CNP either within gaps (forest plots) or between emergent trees (savanna plots). All mist nets measured 12×2.8 m (16 mm mesh; 70 denier/2-ply netting) with four or five shelves. Furthermore, one two-bank harp trap (4.2 m2 capture area; Faunatech) was set in each plot. Each plot was typically sampled for two consecutive nights per field season. Capturing lasted from dusk (ca 18:30) until dawn (ca 06:30). Mist nets and the harp trap were checked every 30–60 min throughout the entire night. We did not capture during nights around full moon phases, and in rare cases interrupted sampling because of heavy rain. Bats were measured (forearm, body mass) and their sex, age, and reproductive status assessed. Most individuals were identified to species in the field and subsequently released. A few bats were sacrificed to check identifications. These synoptic collections are deposited in the Forschungsinstitut Senckenberg, Frankfurt/M., and in the research collection of JF. All adult bats except for insectivores with <10 g body mass and males of Epomophorus gambianus, Epomops buettikoferi, and Hypsignathus monstrosus were individually marked with a stainless steel ball-chain necklace and a serially numbered aluminium band. Males of the three species of fruit bats were not marked as they inflate their throats during courtship calls. The study comprised eight field seasons in TNP between March 1999 and February 2004 (first part of the study [1999–2000]: J. Fahr; second part [2001–2004]: Stefan Pettersson, Göteborg Univ.). In CNP, we sampled bats during seven field seasons between April 1999 and June 2002 (first part of the study [1999–2000]: J. Fahr; second part [2001–2002]: Njikoha Ebigbo, Ulm Univ.). Capture seasons were selected to match similar conditions in phenology and climate, i.e. at the end of the dry season/start of wet season (TNP: Feb/Mar; CNP: Apr/May) and at the end of the wet season/start of dry season (TNP: Aug/Sep; CNP: Oct/Nov). We also captured bats outside plots in an opportunistic fashion with mist nets set in understorey and canopy as well as with harp traps. Such opportunistic sampling (OS) targeted particular habitat types and situations (e.g. small creeks, clearings, and rocky outcrops), which were deemed to yield species that might have been missed in the standardized plots. Additionally, we included data from preliminary surveys in CNP during 1993 and 1995. Total capture effort for CNP and TNP combined was 1765.0 mist net nights and 102.6 harp trap nights (Table 1). Table 1. Capture effort expressed as mist net nights (UN: understorey nets, CN: canopy nets) and harp trap nights (HT: harp traps). 1 net night: one 12 m-net opened for 12 h, 1 trap night: one trap set for 12 h. Opportunistic sampling in CNP includes UN-data from 1993 and 1995. Standardized plots Opportunistic sampling UN CN HT UN CN HT CNP 538.7 155.7 43.1 91.8 4.9 5.0 TNP 512.8 353.4 43.6 63.9 44.0 11.0 Data analysis Recaptures of marked bats were excluded from analysis if they were caught during the same sampling period in the same plot. Estimated species richness (Sest) was calculated with the programs EstimateS 7.5 (Colwell 2005). We followed Brose and Martinez (2004) for the choice of the least biased and most precise estimator to extrapolate estimated species richness, Sest. In a first step, we calculated Sest of a given sample with a suite of non-parametric and parametric estimators of species richness (Abundance-based Coverage Estimator [ACE], Incidence-based Coverage Estimator [ICE], First-order Jackknife [Jack1], Second-order Jackknife [Jack2], and Michaelis-Menten [M-M]). We then calculated the range of sample coverage (observed species richness [Sobs]/estimated species richness [Sest]) and its mean. Variation in estimated sample coverage was generally rather low (8–29%). In a second step, we chose the estimator recommended by Brose and Martinez (2004) as the final estimate of species richness for a given sample. Interpolated species accumulation curves (sample-based rarefaction) of plot data were calculated with the "Mao Tau"-function in EstimateS (Colwell et al. 2004, Colwell 2005). The graphs were rescaled by individuals, resulting in individual-based rarefaction curves sensu Gotelli and Colwell (2001). Rescaling by individuals allows direct comparison of species richness as opposed to rarefaction curves scaled by samples, which represent species density (Gotelli and Colwell 2001). We used 95% confidence intervals to test for significant differences in species richness. We followed Hill (1973) and Jost (2006) in the use of effective number of species when reporting diversity measures other than species richness. In short, the effective number of species equals species richness if all species of a sample have the same frequency and decreases with declining evenness of a sample. Shannon diversity, which is equivalent to Hill's (1973) N1 diversity index, was calculated as eH, with and Simpson diversity was calculated as 1/D, with where pi=the proportion of individuals in the ith species. We also employed the nonparametric estimator of Shannon entropy implemented in Spade 3.1 (Chao and Shen 2006), which accounts for unseen species in a sample, thus resulting in Shannon diversities (eĤ [est]) that are unbiased by sample size. We stress that frequency data derived from captures represent relative abundances of individuals, which in turn are affected by sampling bias of capture techniques. Since our sampling protocol was standardized, comparisons within our study system are valid since data are affected by the same bias. Species richness was used to assess the total number of species in a sample ("diversity of order 0" sensu Jost 2006), Shannon diversity was employed as a diversity measure that weighs species directly proportional to their frequencies ("diversity of order 1"), and Simpson diversity was used as a complement to focus on the most frequent species in a sample ("diversity of order 2"). Evenness was calculated as E=eH/S, where S=number of species in a sample (Buzas and Hayek 1996). Evenness equals 1 if all species in a sample have the same frequency and decreases as samples are increasingly dominated by a few species, hence reducing the effective number of species (eH). We calculated observed evenness from observed Shannon diversity and Sobs as well as estimated evenness (Ê) derived from eĤ and Sest. For the latter, estimators were chosen based on sample coverage, see above. Spatial variation in beta diversity To analyse whether variation in assemblage composition among sites within a region (variation in beta diversity, Tuomisto and Ruokolainen 2006) is explained by geographical location (spatial autocorrelation), we ran independent Mantel tests for each study region. Plot data (relative species abundances per plot) were transformed as dissimilarity matrices based on the quantitative Sørensen (Bray-Curtis) index. Geographical locations of plots had been measured with a hand-held GPS (Garmin GPS II plus) and the Euclidean distances between the midpoints of each plot were calculated with ArcView 3.2a. We compared both distance matrices of each study region, using PC-Ord 5 (McCune and Mefford 2006) to run 10 000 Monte Carlo randomizations. Additive partitioning The program PARTITION (Veech and Crist 2009) was employed to assess additive partitioning of species richness for each study region (CNP, TNP). We tested the null hypothesis that the observed components of diversity at increasingly higher levels (α1, β1, β2,…, βi) could have been obtained by the random placement of individuals among samples at all hierarchical levels (Crist et al. 2003, Crist and Veech 2006). For this approach, the observed numbers of individuals of each species are randomly placed among samples at the lowest hierarchical level, and these samples are then grouped into progressively larger samples at each higher level. Under this null model, each species is not constrained to a particular sample (in our case representing a specific habitat type) but reshuffled among samples, thereby effectively removing the influence of species-specific associations with a particular habitat type (see also Veech and Crist 2007). The program PARTITION places individuals randomly in samples while preserving the original species-abundance and sample-size distribution. For randomizations (10 000), we arranged our samples (plots) in replicates corresponding to their spatial location in the landscape, thus matching a nested design. For TNP, this resulted in three replicates each containing two samples representing distinct habitats (hill forest, swamp forest). For CNP, this resulted in two replicates each containing three samples representing distinct habitat types (forest islands, gallery forests, savannas). Samples were weighted by the relative number of individuals in each sample, i.e. each sample received a weight equal to the individuals in this sample divided by total number of individuals. Our analytical design spanned three hierarchical levels from the local to the landscape scale (Fig. 1). Additive partitioning allows the expression of the proportional contributions of diversity at each level in this hierarchy. Since diversities are calculated as an average of the samples at a given level regardless of how they are nested within the next higher level, this approach is robust to unbalanced sampling designs (Summerville et al. 2003). Although the number of structural vegetation types, and therefore our sampling design, differed between CNP and TNP, the relative contributions of diversity components across spatial scales can be compared between both study regions. Figure 1Open in figure viewerPowerPoint Schematic representation of the hierarchical levels studied in CNP. For TNP, the arrangement differed in that there were two habitat types (rather than three) and three replicates (rather than two). The right-hand circles illustrate how each lower level adds to the next hierarchical level (γ=α1+β1+β2). Functional group composition Bats were classified into five broad functional groups following Schnitzler and Kalko (2001) based on diet (frugi- and nectarivorous [F] vs animalivorous [A]), foraging mode (gleaning [g] vs aerial [a]) and habitat (degree of structural clutter: narrow space [NS; foraging within dense vegetation], edge and gap [EG; foraging close to, but not within dense vegetation], open space [OS; foraging distant from vegetation]) (Appendix). We assessed whether habitat type structured functional group composition of assemblages by testing whether the observed composition in a given habitat type conformed to a random sub-sample of each study area (TNP, CNP) or if the proportional composition of functional groups shows habitat-specific patterns. We employed the program Resampling Stats (Resampling Stats 2006) to create 10 000 random assemblages for each habitat type. These were constrained by drawing the observed number of species without replacement from the species pool of each study region (TNP: 40 species, CNP: 57 species). Statistical significance was calculated as the proportion of null values greater than (or less than) the observed values. This proportion is a p-value that indicates the probability of obtaining a value as great as (or as small as) the observed value by chance. Results Landscape diversity and sample coverage We captured a total of 75 species, 22 of which were shared between the two study areas (Appendix). We recorded 40 species in TNP, with 32 species caught in plots (P) and eight species captured opportunistically (OS). The total for CNP was 57 species, with 51 species recorded in plots and six species that were found off-plot. Standardized plot data revealed significantly higher species richness for CNP than for TNP when rarefied to the assemblage with the lower number of individuals; thus, at 1569 individuals, TNP had 39 species (upper 95% CI: 42 species) whereas CNP had 50 species (lower 95% CI: 45 species; Fig. 2). The completeness of sampling was similar for both areas as indicated by high sample coverage (Sobs/Sest– CNP: 80.3%, TNP: 78.5%) despite the very unequal number of individuals captured in each study area (Table 2). Including data from opportunistic sampling, estimated sample coverage increased to 88.1–91.5% for CNP and 87.7–95.5% for TNP, respectively. At this level of sample coverage, the magnitude of the higher species richness of CNP compared to TNP was much more pronounced (TNP: 39 species, upper 95% CI: 42 species; CNP: 55 species, lower 95% CI: 51 species). Figure 2Open in figure viewerPowerPoint Estimated number of species (extrapolated species accumulation: mean Jackknife 1±1 SD) for CNP (black diamonds) and TNP (grey circles), and sample-based rarefaction curves (interpolated species accumulation: Sobs) with 95% confidence intervals rescaled by individuals. Table 2. Species richness of CNP and TNP broken down to approach (P: plots, OS: opportunistic sampling, pooled: P+OS) and method (UN: understorey net, CN: canopy net, HT: harp trap, combined: UN+CN+HT). Species numbers in brackets refer to those captured with a single method. Sobs: observed species richness, Sest: estimated species richness, eH: observed Shannon diversity, eĤ: estimated Shannon diversity, Eobs: observed evenness, 1/D: Simpson diversity. Sest– a: Michaelis-Menten, b: ICE, c: Jackknife 1, d: Jackknife 2. In CNP, one additional species (Nycteris gambiensis) was found in its day roost, another additional species was found in its day roost and recorded by its echolocation calls (Rhinolophus landeri); in TNP, one additional species (Myotis bocagii) was captured when flying into station building. Comoé NP Taï NP Combined UN pooled CN pooled HT pooled Combined UN pooled CN pooled HT pooled pooled P OS pooled P OS Pteropodidae 9 9 9 9 9 1 8 8 7 7 8 (1) 2 Emballonuridae 1 1 1 – 1 (1) – 1 1 – – 1 (1) – Nycteridae 5
Referência(s)