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Pdf the United Nations, Rome, Italy, Pp Research Article ii FF o o r r e e s s t t doi: 10.3832/ifor1779-008 Biogeosciences and Forestry vol. 9, pp. 354-362 Does degradation from selective logging and illegal activities differently impact forest resources? A case study in Ghana Gaia Vaglio Laurin (1-3), Degradation, a reduction of the ecosystem’s capacity to supply goods and ser- William D Hawthorne (2), vices, is widespread in tropical forests and mainly caused by human distur- Tommaso Chiti (1-3), bance. To maintain the full range of forest ecosystem services and support the (1) development of effective conservation policies, we must understand the over- Arianna Di Paola , all impact of degradation on different forest resources. This research investi- (1) Roberto Cazzolla Gatti , gates the response to disturbance of forest structure using several indicators: Sergio Marconi (3), soil carbon content, arboreal richness and biodiversity, functional composition Sergio Noce (1), (guild and wood density), and productivity. We drew upon large field and re- Elisa Grieco (1), mote sensing datasets from different forest types in Ghana, characterized by Francesco Pirotti (4), varied protection status, to investigate impacts of selective logging, and of (1-3) illegal land use and resources extraction, which are the main disturbance Riccardo Valentini causes in West Africa. Results indicate that functional composition and the overall number of species are less affected by degradation, while forest struc- ture, soil carbon content and species abundance are seriously impacted, with resources distribution reflecting the protection level of the areas. Remote sensing analysis showed an increase in productivity in the last three decades, with higher resiliency to change in drier forest types, and stronger producti- vity correlation with solar radiation in the short dry season. The study region is affected by growing anthropogenic pressure on natural resources and by an increased climate variability: possible interactions of disturbance with climate are also discussed, together with the urgency to reduce degradation in order to preserve the full range of ecosystem functions. Keywords: Tropical Forest, Remote Sensing, Degradation, Logging, Guild, Africa Introduction ne et al. 2012) and fine root production (Asase et al. 2012, Poorter et al. 2008), and Forest degradation is defined as a reduc- (Ibrahima et al. 2010). In Ghana, conces- differentiated according to forest types tion of the ecosystem’s capacity to supply sions interest more than half of the reser- (Bongers et al. 2009). For functional com- goods and services (FAO 2006). The West ves (Hawthorne & Abu-Juam 1995). Im- position, a drought-driven shift toward African forest belt is a biodiversity hotspot pacts on resources other than carbon, such drier forests has been reported by Fauset and a provider of fundamental ecosystem as tree species richness, diversity, func- et al. (2012); while for net primary produc- services, now degraded and reduced to a tional composition, and productivity are tivity, both climate-driven global increase patchwork of forest fragments (CEPF less clear: they are difficult to quantify and (Nemani et al. 2003) and intact African fo- 2003). Selective logging is possibly the pri- vary with disturbance type, intensity and rests biomass increase (Lewis et al. 2009) mary cause of degradation in African fo- the area under analysis. With respect to have been reported. Selective logging is rests with recognized impacts on carbon biodiversity, varied research results were also known to decrease productivity throu- stocks (Cazzolla Gatti et al. 2015, Hawthor- reported for disturbed forests in Ghana gh larger trees removal (MEA 2005). Our research investigates the effects of selective logging and illegal land use and (1) Impacts of Agriculture, Forests and Ecosystem Services Division, Euro-Mediterranean resources extraction (here defined as “ille- Center on Climate Change (IAFES-CMCC), v. Pacinotti 5, I-01100 Viterbo (Italy); (2) Depart- gal activities”) on forest resources, analy- ment of Plant Sciences, University of Oxford, South Parks Road, Oxford OX1 3RB (United zing: forest structure and biomass; soil car- Kingdom); (3) Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), bon content, tree richness and biodiversity, University of Tuscia, I-01100 Viterbo (Italy); (4) Interdepartmental Research Center of Geo- functional composition (guild and wood matics (CIRGEO), Land, Environment and Agriculture and Forestry Department (TESAF), Uni- density); and productivity. With recent versity of Padova, I-35020 Legnaro, Padua (Italy) field data from two national parks and two forest reserves, we investigated whether @ Francesco Pirotti ([email protected]) the considered disturbance drivers diffe- rently affected forest resources, and the Received: Jul 26, 2015 - Accepted: Nov 06, 2015 effectiveness of protection measures. With historical field data from the two reserves, Citation: Vaglio Laurin G, Hawthorne WD, Chiti T, Di Paola A, Cazzolla Gatti R, Marconi S, we analyzed temporal changes in basal Noce S, Grieco E, Pirotti F, Valentini R (2016). Does degradation from selective logging and area and guild composition. Thirty years of illegal activities differently impact forest resources? A case study in Ghana. iForest 9: 354- remote sensing data allowed to study pro- 362. – doi: 10.3832/ifor1779-008 [online 2016-01-29] ductivity trends in ten forests, mostly dis- turbed reserves, of southwestern Ghana Communicated by: Matteo Garbarino and their relationships with precipitation, temperature and cloud cover. © SISEF http://www.sisef.it/iforest/ 354 iForest 9: 354-362 Vaglio Laurin G et al. - iForest 9: 354-362 y r We tested the following four hypotheses: Ordination scores for tree species (from 1986). Samples were oven-dried (60 °C) to t s (i) impacts on forests’ mean structure pa- Hall & Swaine 1976, 1981) weighted by a constant weight and mineral soil was sie- e r rameters, mean soil carbon (C) content, stem abundance were used to obtain sco- ved at 2 mm to remove rock fragments; o tree species’ richness and diversity, and res for plots (Hawthorne 1996). This appro- they were then ground in a ball mill to F guilds and wood density, were stronger in ach was used by Hawthorne (1995) to reach the highest homogenization possible d n the less protected areas; (ii) a significant define a wet to dry gradient across Ghana. and measured for total carbon via dry com- a ® increase in basal area occurred in the re- We ordinated plot data using the Nonme- bustion (ThermoFinnigan Flash EA112 CHN, s e serve (Bia) where logging activities stop- tric Multidimensional Scaling (NMS) statis- Gemini BV, Apeldoorn, Netherlands). To c n ped about two decades ago, while its sig- tical method using the PCORD ver. 6 soft- calculate the amount of carbon (C) each e i nificant decrease occurred in the reserve ware (McCune & Mefford 2011). sample was corrected for the amount of c s affected by continuous illegal activities; (iii) From field records we calculated: wood rock fragments, and the soil organic car- o a change in guilds composition in the direc- density (WD, Global Wood Density Data- bon (SOC) stock calculated considering the e g tion of recovery occurred in the logged base – Chave et al. 2009); basal area (BA); bulk density, the C concentration and the o i reserve, while a change toward further above ground biomass (AGB, Chave et al. depth of each layer (Boone et al. 1999). Sig- B degradation occurred in the illegally dis- 2005); and tree guilds according to the nificant differences (α = 0.001) between C – turbed one; (iv) the productivity in the ten Hawthorne (1993, 1995, 1996) classifica- stocks of different soil layers were tested t s forests increased over the last 30 years. tion, which identifies pioneer (P), non-pio- within each site and among sites by one- e r neer light-demanding (NPLD), shade-bea- way analysis of variance and Tukey’s test o F Methodology rer (SB), and swamp-resistant (SR) species. (Sokal & Rohlf 1995). i We used 96 recent (2012-2013) and 94 his- Mean parameters difference among areas torical (1987 and 1991) field plots. In the were tested in recent data using the Mann- Species richness and biodiversity recent dataset we collected species’ name, Whitney (MW) U-test and the Kolmogorov- To avoid area-effects on results, richness diameter at breast height (DBH), and Smirnov (KS) test to additionally detect dif- and biodiversity indices were computed height (H) information in four areas in a ferences in the shape of distributions (Hol- using recent data from subplots of same ~150 km range on a south to north gradient lander & Wolfe 1973). We preferred non-pa- area, equal to 500 m2 in Ankasa NP and 400 (blue squares in Fig. S1 - Appendix 1): (i) the rametric statistics to account for non-nor- m2 in all the other sites (Tab. S3 - Appendix wet evergreen Ankasa National Park (NP), mal distributions and outliers, represented 1). We adopted the Margalef’s richness in- where logging was scarce and confined to by large old trees. The two DBH classes (10- dex because it is less sensitive to sampling the southern range; (ii) the moist ever- 20 cm and over 20 cm) were tested sepa- efforts variations (Margalef 1958). Conside- green Dadieso Forest Reserve (FR), sur- rately at 95% confidence level, using the ring the high number of rare species, ex- rounded by and including communities and normal approximation to compute the p- pressed as singletons, doubletons, and uni- cocoa farms which cause frequent illegal value, as samples are large in statistical que species (Magurran 2004), we adopted activities, such as timber extraction, hunt- terms. Distributions of H, DBH, and WD the Chao 1 index (Chao 1984) as a diversity ing, gaps opening for cultivation, with pres- were estimated with a kernel density me- measure, useful when datasets are skewed ence of swampy zones; (iii) the moist ever- thod based on the Gaussian kernel and toward the low-abundance classes (Chao & green Bia Resource Reserve (RR), selecti- using the Silverman’s rule for bandwidth Shen 2003).
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