Research Article ii FF o o r r e e s s t t doi: 10.3832/ifor2056-010 Biogeosciences and Forestry vol. 10, pp. 362-368
Tree species diversity of three Ghanaian reserves
Roberto Cazzolla Gatti (1), Among tropical areas, Africa is considered to be poor in terms of biodiversity Gaia Vaglio Laurin (2-3), as compared with Amazon or South-East Asia, especially with respect to forest Riccardo Valentini (2-3) diversity. Despite this lower diversity, some African tropical zones, such as Ghana, harbour a plethora of species, particularly of trees. Unfortunately, as a result of anthropogenic impacts, biological diversity in West Africa dramati- cally decreased in the last decades, with very limited reference to evaluate the amount of the loss. Due to these growing pressure, a collection of relevant biodiversity information in this region seems to be urgent. We surveyed 127 temporary plots randomly distributed within 3 protected areas in Ghana and we collected data on tree (dbh>10 cm) species richness and their abundances. We also performed α, and β diversity analyses, and estimated the effective number of species, adopting various indices and approaches to provide further information on each assemblage. The main goals of this research were: (i) to provide a wide tree species database (abundance-based data), together with some biodiversity analyses; (ii) to estimate the sampling effort needed for next biodiversity surveys in the same and similar regions; and (iii) to calculate some indices useful to monitor the future of these protected areas both in terms of conservation and biodiversity research.
Keywords: Ghana, Protected Areas, Forests, Database
Introduction and monitor some of the most remote severely disturbed by anthropogenic activi- Biological diversity is critical for the main- places on Earth (Danielsen et al. 2000); and ties for many centuries (Valentini et al. tenance of ecosystems (Haines-Young & (iii) the high level of complexity of tropical 2014). In particular, West African forests Potschin 2010) and each species plays a ecosystems (Bonn & Gaston 2005). Among were overexploited for timber exportation fundamental role (Jones & Lawton 2012). tropical areas, Africa is one of the least and agricultural development during the The presence of different species ensures studied regions, especially in terms of rain- European colonization (Barnes 1990). The ecosystem resilience and the ability to forest diversity (Mayaux et al. 2013). More- result is a fragmented landscape from keep life conditions within tolerance levels over, different protocols for surveying bio- Guinea to Nigeria, composed by patches of around an oscillating equilibrium (Lenton & diversity in different regions of the world forest interspersed in a rural environment Lovelock 2000). The XXI century is experi- have been adopted, making difficult to (Vaglio Laurin et al. 2013). Consequently, encing a dramatic decline of global biologi- compare studies and datasets (Gotelli & biological diversity dramatically decreased cal diversity, which is more evident in tropi- Colwell 2001). in the last few decades, with very limited cal regions (Cazzolla Gatti 2016a), and it is Timber extraction is the principal anthro- reference to evaluate the amount of the mainly due to increasing anthropogenic pogenic impact and the main cause of loss (Dupouey et al. 2002). Due to the impacts (Battipaglia et al. 2015, Cazzolla species extinction in terrestrial forest eco- growing anthropogenic pressure in the Gatti 2016b). systems (Harvey & Pimentel 1996), and West African region, it seems urgent to col- Biodiversity assessments in some tropical even selective logging has been ques- lect relevant biodiversity information. areas are scarce and data are scanty, and tioned for its negative effects on both ani- These data could be used as a baseline for this is due to different factors: (i) the mal and plant diversity (Fredericksen & future studies aimed at the analysis of envi- plethora of species living in tropical forests Fredericksen 2002, Cazzolla Gatti et al. ronmental changes (Mace & Baillie 2007). and reef barriers (Connell 1978, Cazzolla 2015, Vaglio Laurin et al. 2016). African Tree diversity is often considered as a Gatti 2016c); (ii) the difficulties to reach tropical forests have been exploited and good proxy to estimate diversity of other taxonomic groups (Gentry 1988). Because tree diversity can be measured at different (1) Biological Diversity and Ecology Laboratory, Bio-Clim-Land Centre of Excellence, levels, in this research we evaluated three Tomsk State University (TSU), 36 Lenin Prospekt, Tomsk, 634050 (Russia); (2) Impacts of Agri- biodiversity components, in order to pro- culture, Forests and Ecosystem Services Division, Euro-Mediterranean Center on Climate vide an exhaustive biodiversity informa- Change (IAFES-CMCC), v. Pacinotti 5, I-01100 Viterbo (Italy); (3) Department for Innovation in tion: (i) α diversity, as the mean species Biological, Agro-Food and Forest Systems (DIBAF), University of Tuscia, I-01100 Viterbo (Italy) diversity of each study site; (ii) β diversity, as both the difference in diversity among @ Roberto Cazzolla Gatti ([email protected]) sites and among plots at each site; and (iii) effective species number, as the overall Received: Mar 14, 2016 - Accepted: Jan 03, 2017 species richness in each study site. On a theoretical basis, at the spatial scale Citation: Cazzolla Gatti R, Vaglio Laurin G, Valentini R (2017). Tree species diversity of three of a hectare or less, evolutionary-biogeo- Ghanaian reserves. iForest 10: 362-368. – doi: 10.3832/ifor2056-010 [online 2017-03-07] graphical influences and variation in physi- cal factors are constrained to a minimum Communicated by: Marco Ferretti (Levey et al. 2002). For instance, one such set of interactions is described by the Jan-
© SISEF http://www.sisef.it/iforest/ 362 iForest 10: 362-368 Cazzolla Gatti R et al. - iForest 10: 362-368
y 2
r zen-Connell mechanism, which proposes km (Fig. S1 in Supplementary material) and Biodiversity data sampling
t 2
s that the probability of survival of a seed or comprises Bia National Park (NP, 77 km in Field data were collected in the above- e
r the successful establishment of a seedling the northern part) and Bia Resource mentioned study sites between 2011 and 2 o increases with distance from its parent Reserve (RR, 228 km in the southern part). 2013 in the framework of different research F tree. Seeds that fall relatively far from the More than 300 plant species per hectare projects, retaining for the present analysis d n parent increase survival, as they grow dis- can be observed. Species beloging to only variables common to all the surveys a tant from predators, herbivores and/or Makore, Dahoma, Khaya and Marantis gen- and collected with same protocols. Identifi- s e pathogens which affect mother plants era are widespread (see also Appendix 1 in cation of tree species was conducted with c
n (Janzen 1970, Connell 1971). In micro-scale, Supplementary material). the help of the same botanists in all areas e i Janzen-Connell’s idea could explain the Dadieso Forest Reserve (FR) lays in the (Appendix 1). c
s high tree diversity observed within small north of Boin river FR and Disue FR (Fig. S1 Rectangular (20×25 m in Ankasa CA) or o plots in tropical areas, but further basic in Supplementary material). The FR is square (20×20 m in Bia NA and Dadieso FR) e g and detailed data on tropical tree diversity included in the Aowin Suaman District spatially separated plots were arranged in
o 2 i are needed to test this and other hypothe- Assembly jurisdiction. The area is 171 km each study site with a random placement
B 2 ses. and comprises 4.50 km , along the border protocol (Magurran 2013, Messina et al. – The objectives of this study are: (i) to pro- with Ivory Coast, in which farms are admit- 2016), within a circular area of 1 km of t s vide baseline information on tree diversity ted. The vegetation is transitional between radius, established in the geometrical cen- e r in the form of a wide database for the moist evergreen and wet evergreen types. ter of each reserve. o
F three protected areas surveyed in Ghana; Swamps are common throughout the for- For all study sites we chosen a minimum i (ii) to estimate the minimum sampling est and in some areas they cover hundreds common threshold of diameter at breast effort needed to collect the effective num- of hectares. Some species are common in height (dbh) of 10 cm, as this encompass- ber of species in other comparative studies this forests reserve, such as Berlinia tomen- ed the main diversity of the analysed tropi- in the same or ecologically similar pro- tella, Calpocalyx brevibracteatus and Strom- cal forest, and 5
363 iForest 10: 362-368 Ghana forest diversity y
the Abundance-Coverage Estimator (ACE) r
Fig. 1 - Species- t
indexes (Magurran 2013). These measures s
area curves (a), e
can provide an approximation of the ex- r Coleman’s rar- pected tree diversity in each area, and can o F also be considered as a proxy of the effec- efaction curves (b) and fre- d tive number of species (Magurran 2013). n
quency-abun- a SACs are sources of reliable information to dance distribu- s adequately sample the assemblages (Go- e tions (c) for the c
telli & Colwell 2001). n
three study e
We derived Coleman’s rarefaction curves i
areas. Error c to evaluate the number of species at the s same abundances level, thus comparing bars represent o the standard e the richness of three study areas character- g
deviation. o ized by different sampling efforts. We also i B plotted the frequency-distribution graph to – analyse dominance/evenness patterns, and t the rank-abundance (Whittaker’s) plot to s e evaluate the total richness, the abun- r o
dances distribution, and to evidence differ- F ences in evenness among the assemblages. i We tested the significance of the abun- dances distribution patterns by the Kol- mogorov-Smirnov statistical test (here at 95% confidence level). Together with the Whittaker’s plots we show the Empirical Cumulative Distribution Function (ECDF) graph, which allows a bet- ter discrimination of different assemblages obtained by rescaling the ranks according to the richness (Magurran 2013). β-diversity analysis were performed com- puting the Marczewski-Steinhaus index to understand the dissimilarity between pairs of sites (i.e., β-diversity among sites). Be- cause this measure considers only species presence and not species abundances data, it is not influenced by the dominance of certain species in the assemblage (Ma- gurran 2013). Thus, we also computed the complement of Morisita-Horn abundance- based β-diversity measure (1-CMH) to under- stand which influence the abundance of species can have on similarity. β-diversity was also calculated among plots of each study site (β-diversity within site) to evalu- ate “internal” small-scale diversity pat- terns. The effective number of species was esti- mated at study area level by the Chao1 index and the Abundance-Coverage Esti- species sampled in this area. The guilds minimum values of about 3 ha in each of mator (ACE – Magurran 2013). percentages analysis shows that Ankasa the three sites. was the only site characterized by a domi- Coleman’s rarefaction curves, obtained Results nant guild reaching 60%, with almost 20% of after rarefaction at the minimum sample shadow tolerant species and a low amount size (equal to 384 individuals), indicated Richness, α diversity and guild (<10%) of pioneer species. Bia and Dadieso, that Dadieso was the richest site, while An- composition instead, had a lower percentage (≈ 40%) of kasa was intermediate and Bia was the The list of species and their abundances dominant species and a similar level of poorest (Fig. 1b). The frequency-distribu- in each study site are reported in Tab. S1 shadow tolerant species, while pioneers tion plot (Fig. 1c) evidenced the differences (Supplementary material). The results illus- ranged between 15-20%. The swamp guild among the study areas. Ankasa has well trated in Tab. 1 indicates that Bia and Da- was more represented in Ankasa (≈ 7%) represented frequencies in 3-4, 5-8 and the dieso are very similar in α-diversity and than in the other two sites (2.50% and 2.59% following abundance classes, which slightly guilds’ percentage composition, while An- in Dadieso and Bia, respectively). decline. Dadieso and Bia showed lower fre- kasa differs from both. Although the sam- quencies in classes other than 1-2 and zero pling effort in Ankasa was the smallest, Accumulation and rarefaction curves, frequency at abundance value higher than species richness (147) of this CA was higher and abundance-frequency distribution 33-64 individuals per species. Moreover, by 19 and 27 species, respectively, than Bia Ankasa showed the upper SAC curve, Bia showed a bimodal distribution that and Dadieso. Margalef, ACE and Chao1 in- even considering the standard deviation peaks at 1-2 and 9-16 classes of abundance. dices of Ankasa were also the highest. buffers (Fig. 1a). The minimum sampling The number of singletons recorded in area needed to achieve a representative Species-abundance curves, rank- Ankasa was also greater than the other collection of species at each site is pre- abundance plots, and ECDF two areas, with 54 singletons out of 147 sented in Tab. 2. This estimation indicates Cumulative abundance of species was iForest 10: 362-368 364 Cazzolla Gatti R et al. - iForest 10: 362-368 y r
t Tab. 2 - Minimum sampling area estimation. s e r Area Area o 1/z F Study site log Chao 1 saturation ACE saturation c c z (Rad z) d Chao 1 (ha) ACE (ha) n a Ankasa CA -0.225 0.595 0.57 1.754 213 3.02 206 2.84 s
e Dadieso FR -0.895 0.127 0.71 1.408 195 3.07 188 2.92 c
n Bia CA -0.374 0.422 0.59 1.694 200 3.43 193 3.22 e i c s o Fig. 2 - Abun- plotted against the species ranked accord- e g dance-species ing to their abundances (Fig. 2a). The x- o i curve (a), rank- axes level of the inflection point indicates B abundance the quota of dominant species. – plots (b) and Fig. 2b shows the Whittaker’s plots of the t s ECDF (c) for study areas. Ankasa has a lightly steeper e r the three curve than Bia and Dadieso. Being the rich- o
F study are-sites. ness estimated from the x-axes point i n/N is the rela- where the curves end, this diagram re- tive abun- marks the increasing richness from Dadie- dance. so, to Bia and then to Ankasa. The distribu- tions of abundances were not significantly different (p>0.05) for any CA pair after two-samples Kolmogorov-Smirnov test (Dadieso-Ankasa D[120, 147] = 0.09; Ankasa-Bia D[147, 128] = 0.05; Bia-Dadieso D[128, 120] = 0.08). The ECDF for the three areas is shown in Fig. 2c. Plotting the assemblages with this function, Ankasa curve was still higher than those of Bia and Dadieso. Ankasa curve was also steeper, which means that it is less even than the other two CAs. The lower left part of the curves show that Ankasa and Bia accounted for more rare species than Dadieso, which is upper posi- tioned.
Effective number of species and β diversity The ACE and Chao1 indices (here consid- ered as proxy of the effective number of species) were the highest for Ankasa, Bia values were intermediate and those of Dadieso the lowest (Tab. 1). The minimum sampling area estimates, which is based on those measures, showed a value of about 3 ha to collect the expected number of spe- cies for each of the study sites (Tab. 2). Marczewski-Steinhaus and complement Morisita-Horn indices calculated among paired study areas shows that Bia-Ankasa pair was characterized by the highest β diversity, while Dadieso-Bia pair by the low- est, and Ankasa-Dadieso stands in the mid- dle (Tab. 3a). The same indexes calculated within each area shows that the highest internal β diversity was found in Dadieso, while the lowest was in Ankasa, with inter- Tab. 3 - Among (a) and within (b) study areas β diversity. (SD): standard deviation. mediate values in Bia (Tab. 3b). Discussion Marczewski-Steinhaus Morisita-Horn (1-CMH) (a) We provided tree diversity data for three Ankasa Bia Ankasa Bia protected areas in Ghana, including infor- Dadieso 0.77 0.59 0.81 0.51 mation on the abundances of species, in a Ankasa - 0.79 - 0.87 region where field data are sometimes (b) Mean ± SD Mean ± SD scarce and relatively difficult to collect. The Ankasa 0.86 ± 0.06 0.73 ± 0.16 evaluation of different indices and mea- Bia 0.91 ± 0.08 0.84 ± 0.16 sures allowed to preliminary characterize part of the diversity of the areas, also pro- Dadieso 0.95 ± 0.07 0.93 ± 0.12 viding indication on the minimum sampling
365 iForest 10: 362-368 Ghana forest diversity y
effort needed to collect the effective num- the low disturbance in Ankasa results from study 82-89 species/500 individuals were r t
ber of species in these and similar forests, the comparison of the abundance-fre- collected in Ghana, while applying rarefac- s e
which could encompass and include the quency distributions and Whittaker’s plots tion to the same number of individuals on r species missed by our pilot study. of the three study areas. our data we found 114-120 species/500 indi- o F Since only a portion of the reserves was Nevertheless, due to sampling limitations viduals in all the three sites. Unexpectedly, d considered, results cannot be extrapolated and the consequent lack of a full collection these values are in line with most of Ama- n a to the entire areas, but this study should of very rare species, in each protected area zonian sites (Costa Rica, Venezuela, Ecua- s be considered as a pilot report in order to the left side of abundances-frequency dis- dor and Brazil, in particular) reported by e c
provide background information for future tributions is hidden (Preston 1948). Phillips et al. (1994), with the only excep- n e research. Moreover, sample plots of differ- The Kolmogorov-Smirnov test evidenced tion of Peru. South-east Asian forests show i c
ent shape and size could have caused pos- no significant difference in the distribu- always higher numbers of tree species. This s sible bias. For instance, Ankasa CA, with tions of the abundances of species among preliminary comparison suggests that the o e the largest and rectangular sample plots, the three sites. It follows that, although tree diversity of African forests might be g o was also the richest, and this may be at different in vegetation type, logging his- reconsidered, as it could be comparable i B least partially due to different shape and tory and climatic zone, the species compo- with that of some South American sites – size of the plots. sition of these protected areas can be eas- when individuals, instead of area, are taken t
Most of the indices and graphs indicate ily compared in terms of their diversity. into account. This underlines that tropical s e that Ankasa is the richest site among those This could be the reason why we obtained forest diversity is not always a matter of r o
surveyed, although the sampled area is the the same minimum sampling area of about quantity (i.e., number of species in area F smallest among sites. This confirms what 3 hectares in every study areas. This value units), but sometimes an issue of quality i was observed by Martin (1991) that this is much higher than what was previously (i.e., how individuals distribute in space Conservation Area has the highest tree adopted in other studies on tree diversity and among species – Cazzolla Gatti 2011, richness level found in West Africa. While of the region (Asase et al. 2012, Appiah Cazzolla Gatti 2016d). most of the tested indexes, measures and 2013). We estimate that the mean effective The within plots β-diversity of Dadieso is curves have higher values in Ankasa (e.g., number of species (Colwell & Coddington much higher than those of the other two Margalef, ACE, Chao1, rare species, N, S, 1994) that can be collected in a minimum forests. This means that this forest reserve SAC curve, ECDF), the Shannon and Simp- sampling areas of 3 hectares in each site is has a great spatial diversity of species rich- son diversity measures for this area are, about 200 (SChao1 ± SD = 202.67 ± 9.29; ness, which usually reflects the hetero- instead, the lowest among the three sites, SACE ± SD = 195.67 ± 9.29). If considered geneity of the environment. Indeed, Dadie- and Alpha-Fisher shows an intermediate together, a total of 250 species and 1868 so is a transitional forest that preserves value. This result was expected as these individuals were recorded within the three both moist evergreen and wet evergreen diversity measures are strongly influenced protected areas (see Tab. S1 in Supplemen- species, including species typical of by the abundances of dominant species: tary material for the plot-based abundance swamps. the presence of few dominant and many data). The highest β-diversity between sites was rare species produces a low evenness. This We looked at SACs to locate the number found for the Ankasa–Bia pair and this is also evidenced in Ankasa by the species- of species per hectare we collected in our diversity evidences the climatic and ecolog- abundance curve. Coleman’s rarefaction study sites, which is in the range of 88-118. ical differences (wet vs. moist evergreen) curves show Ankasa in an intermediate Then we compared these values to some between these forest types. Dadieso has position, between Dadieso and Bia. We other tropical forest sites in the Amazon intermediate values when paired with both argue that the random removal of almost and in tropical Asia, where tree (dbh≥10 Ankasa and Bia, reflecting the mixed vege- half of the individuals through rarefaction cm) species number per hectare reached tation type that characterizes this reserve. procedure (from the 841 collected in values of 300 (Valencia et al. 1994) and 200 In conclusion, the data and indices we Ankasa to the rarefaction value of 384.52) (Parmentier et al. 2007), respectively. provide could be useful to address next proportionally reduced the richness of As mentioned before, the results of this studies on these and similar sites, and can uneven sites more than the richness of study are preliminary and should be consid- be considered as a reference in order to even ones. As documented in other tropi- ered with caution, as only a small portion monitor the future of the surveyed pro- cal and complex ecosystems (Stirling & of the sites have been sampled and our tected areas. Wilsey 2001, Cazzolla Gatti et al. 2015), rich- data could not be representative of the ness measures do not always match the whole reserves. Nonetheless, we found Acknowledgments diversity indexes, and it is advisable to that all the three protected areas we sur- We acknowledge the ERC grant Africa carefully consider both richness and diver- veyed are richer than other other African GHG #247349 for providing research fund- sity measures. The occurrence of swamps tropical forests, where the number of tree ing. We are very grateful to the Ghana in Ankasa creates a diversification of this species with dbh ≥ 10 cm per hectare was Forestry Commission staff, the botanist Mr ecosystem, with a consequent addiction of no more than 73 (Whitmore & Sidiyasa Ntim Gyakari, Justice Mensah, and all the species in the related guild, which also con- 1986). A more recent study (Phillips et al. collaborators who supported the field acti- tributes to the high richness of this site. 1994) analysed different tropical forests, vities in Ghana. Moreover, the total number of species we reporting values of 86-92 tree species per collected in Ankasa is higher than those hectare in Ghana and 56 in Uganda. This References recorded by other studies in the same area confirms that Bia and Dadieso forests are Appiah M (2013). Tree population inventory, (Asase et al. 2012). within the range of other Ghanaian forests diversity and degradation analysis of a tropical A major difference among the study sites as for species number, but still richer than dry deciduous forest in Afram Plains, Ghana. is the presence of pioneer species. Even if other tropical African sites. Contrastingly, Forest Ecology and Management 295: 145-154. - untouched forest can also harbour some of Ankasa is always well above the richness of doi: 10.1016/j.foreco.2013.01.023 them, pioneers are considered as an indica- all Ghanaian and most of African forests. Asase A, Asitoakor BK, Ekpe PK (2012). Linkages tor of disturbance and degradation (Ko- To better evaluate richness similarities between tree diversity and carbon stocks in hyama 1993). Ankasa showed a much and differences, in order to reduce the bias unlogged and logged West African tropical lower percentage of pioneers than Bia and due to the area effect, we considered the forests. 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367 iForest 10: 362-368 Ghana forest diversity y
ties. Biogeosciences 11: 381-407. - doi: 10.5194/ Supplementary Material Fig. S1 - The three study areas in Ghana, r t
bg-11-381-2014 West Africa. s
Whitmore TC, Sidiyasa K (1986). Composition e Appendix 1 - Study site supplementary in- r
and structure of a lowland rain forest at Toraut, formation. Tab. S1 - Species list and species abun- o F northern Sulawesi. Kew Bulletin 41 (3): 747. - dances in each study site. d
doi: 10.2307/4103127 Appendix 2 - Supplementary methods. n a Link: [email protected] s
Appendix 3 - Supplementary conclusions. e c n e i c s o e g o i B
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