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 , 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 . Results indicate that functional composition and the overall number of 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 (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). We also reported the Cole- vely logged in 1980-90 and possibly also selection (Silverman 1986). For AGB and man’s rarefaction curve (Coleman et al. few years after; (iv) the moist semi-decidu- BA, which are density dependent, we re- 1982) and the Shannon’s evenness (Magur- ous Bia National Park, where there are no ported the normalized cumulative values ran 2004) for comparison purposes; how- logging records but fire, drought and ele- per hectare. The following pairs were tes- ever, both measures are sensitive to spe- phants cause natural disturbance. Histori- ted: Ankasa vs. Bia NPs, with limited distur- cies abundance and may not be optimal for cal basal area and species records were col- bance occurrence; Bia NP vs. Bia RR, with forests with many rare species (Gotelli & lected by the Ghana Forestry Commission different disturbance levels; and Dadieso Colwell 2001). Chao 1 index results were in one hectare plots: 59 in the Bia RR and FR vs. the other three areas. To detect used to estimate the minimum sampling 35 in Dadieso FR (dataset described in changes between recent and historical area needed to detect most of the species Hawthorne 1993). We collected orthopho- data, the Mann-Whitney U-test was used in each site (A), using the area-species tos over the recent plots (10 cm spatial re- for BA and the paired z-test for guild com- curve equation (eqn. 1):

solution) in March 2012 with an aerial sur- position. z vey: we screened for signs of disturbance S =cA (logging trails, large gaps, and irregular ca- Forest soil data nopy height), also reviewing information Soil information was derived from eight where c and z are parameters derived from gathered from rangers and villagers to 50×50 m plots, two per forest area, located the curves and S is the Chao 1 index. check the scarce disturbance official re- in close proximity to recent field plots. The cords. This was done by visual interpreta- soil field data used here are part of a larger Remote sensing and meteorological tion of images and also by interaction with soil data collection realized in the frame- data the local community. work of the FP7 ERC Africa GHG project. As The third generation of the GIMMS3g soil sensors were also used for project (Global Inventory Modelling and Mapping Forest structure and guild data needs, it was decided to realize the soil sur- Studies) NDVI dataset has 15-day temporal In the recent plots, DBH, H, and species vey just outside of the plots, to avoid pro- frequency, 1/12th of a degree spatial resolu- records were collected for trees having blems to sensors and instruments that tion and continuity from July 1981 to De- DBH>10 cm. For the 1600 m2 plots, 400 m2 might be caused by the repeated passages cember 2011 (Zhu et al. 2013). We extracted subplots were defined for sampling also of the tree survey team. Forest and soil NDVI values for 7 reserves and 3 national trees with 10-20 cm DBH (Tab. S1 - Appen- plots were set up in homogeneous areas of parks in southwestern Ghana (red stars in dix 1). Height was collected using a laser small extent. In each plot, 10 samples of Fig. S1 - Appendix 1), using pixels with >75% hypsometer. In total, we sampled 11.79 ha the organic horizon were randomly col- of their area inside the following reser- and 1819 trees for DBH>20 cm and 8.51 ha lected using a 40×40 cm frame. In the mi- ves/parks: Anhwiaso FR, Ankasa NP, Atewa and 1988 trees for DBH in 10-20 cm range neral soil, 10 samples were collected in the FR, Bia RR, Dadieso FR, Kakum NP, Kroko- (Tab. S2 - Appendix 1 ). In historical plots all topsoil (0-30 cm depth) and 10 in the sub- sua NP, Subri FR, Subuma FR, and Tano FR. trees above 30 cm DBH were sampled, and soil (30-100 cm depth), using a cylinder From the Climate Research Unit (CRU) 3.2 trees in the 10-30 cm DBH range were col- (diameter = 5 cm; height = 5 cm) to deter- dataset (Harris et al. 2014) we extracted lected in subplots of 0.2 ha. mine the bulk density (Blake & Hartge monthly rainfall, temperature, and cloud

355 iForest 9: 354-362 Selective logging and illegal activities impact on forest resources in Ghana y

cover for the same period (1981-2011). We r

Fig. 1 - Percentage of t

calculated monthly, seasonal and yearly ba- s

guilds in recent data. e

sic statistics (mean, minimum, maximum, r For trees with diame- standard deviation) for NDVI time series, o F applied a 12-month moving-window avera- ter at breast height (DBH) > 20 cm. (ANP): d ge to visualize trends and oscillations, and n

Ankasa National Park; a detected anomalies subtracting the 30- (DFR): Dadieso Forest s year averaged monthly values from the e Reserve; (BRR): Bia c

observations of that month, and then ap- n

Resource Reserve; e plying a 12-month moving-window average i

(BNP): Bia National c

(Box et al. 2013). The observed differences s in trends among sites were evaluated con- Park. o e sidering also their distance from the center g o of the wet evergreen Ghana ecological zo- i B ne (FAO 1998). We calculated Spearman’s – correlation among NDVI and precipitation, t cloud cover and temperature; and trends in s e

CRU 3.2 and NDVI time series using the r o

Seasonal Modified Kendall test (Hirsch & F Slack 1984) suited to treat data with sea- i sonal cycles, and covariance evaluating the trend magnitude with slope estimation. Results ding to soil moisture content or soil drai- cord was found. Ordination of trees and disturbance nage (Fig. 1). The orthophotos screening evidences showed varied signs of disturbance. Anka- Height, DBH, above ground biomass, The ordination scores attributed to plots sa showed no sign with closed and uniform and basal area corresponded to eight forest subcatego- canopy. In Bia NP, only uneven canopy With recent data we performed descrip- ries (Tab. S4 - Appendix 1) from Hall & Swai- height was detected in two plots. Logging tive statistics for H and DBH (Tab. S5 - ne (1976, 1981). NMS results showed that scars, fallen trees, gaps and different ca- Appendix 1), AGB and BA normalized by our samples represent three different fo- nopy heights were observed in nine Bia RR area (Fig. 2, Tab. S6 - Appendix 1), and rest types found in the region along a pri- plots. Even more signs were found in Da- paired comparison tests at 95% confidence mary latitudinal gradient: wet evergreen dieso (14 plots), where rangers, villagers level (Tab. 1). For mean H, Ankasa resulted (Ankasa), moist evergreen (Dadieso) and and workers from timber companies repor- to be the tallest forest, followed by Bia NP, moist semi-deciduous (Bia Reserve and ted frequent illegal activities (pers. comm. and then Dadieso and Bia Reserves; diffe- NP); a secondary gradient is present, accor- from March 2013), but no quantitative re- rences were significant between all pairs

Tab. 1 - Results of statistical pairwise comparison tests, 2012-2013 data. (ANP): Ankasa National Park; (DFR): Dadieso Forest Reserve; (BRR): Bia Resource Reserve; (BNP): Bia National Park. (H): height; (DBH): diameter at breast height; (WD): wood density; (BA): basal area; (AGB): above ground biomass; (MW): Mann-Whitney test; (KS): Kolmogorov-Smirnov test; (*): p< 0.05; (**): p < 0.01; (***): p< 0.001.

DBH 10-20cm DBH > 20cm Comparison Parameter MW stat p-value KS stat p-value MW stat p-value KS stat p-value BNP vs. BRR H -0.88 - 0.10 - -4.44 *** 0.20 *** DBH 0.84 - 0.06 - -4.23 *** 0.17 *** WD -1.03 - 0.11 - 1.15 - 0.11 * BA 0.84 - 0.06 - -4.23 *** 0.17 ** AGB -0.19 - 0.03 - -4.62 *** 0.20 *** BNP vs. ANP H -14.30 *** 0.50 *** -9.34 *** 0.28 *** DBH 0.63 - 0.05 - 1.90 * 0.11 ** WD -8.29 *** 0.36 *** -12.62 *** 0.44 *** BA 0.63 - 0.05 - 1.90 * 0.11 ** AGB -9.73 *** 0.30 *** -3.95 *** 0.17 *** DFR vs. BNP H -0.27 - 0.08 - 2.22 ** 0.14 ** DBH -2.63 ** 0.14 * 4.56 *** 0.17 *** WD -2.56 * 0.21 *** -3.17 *** 0.21 *** BA -2.63 ** 0.14 * 4.56 *** 0.17 *** AGB -2.43 * 0.15 * 3.36 *** 0.15 *** DFR vs. BRR H 0.55 - 0.10 - -2.75 *** 0.15 *** DBH -3.52 *** 0.16 ** -0.33 - 0.05 - WD -1.21 - 0.14 ** -1.91 * 0.12 * BA -3.52 *** 0.16 ** -0.33 - 0.05 - AGB -2.25 ** 0.13 * -1.99 ** 0.11 * DFR vs. ANP H -15.33 *** 0.49 *** -12.65 *** 0.42 *** DBH 4.29 *** 0.14 *** -3.90 *** 0.16 *** WD -5.04 ** 0.19 *** -8.17 *** 0.30 *** BA 4.29 *** 0.14 *** -3.90 *** 0.16 *** AGB -6.75 *** 0.22 *** -9.01 *** 0.27 *** iForest 9: 354-362 356 Vaglio Laurin G et al. - iForest 9: 354-362 y

r for trees with DBH>20 cm, whereas for t

s those within the DBH range 10-20 cm signif- e

r icant differences were found only between o Ankasa and both Bia NP and Dadieso re- F serve. Regarding average AGB, Ankasa sho- d n wed the highest value, followed by Bia NP, a and Bia and Dadieso reserves; pairwise dif- s e ferences were always significant for both c

n DBH ranges, except between the two Bia e i areas for 10-20 cm DBH trees. c

s Mean BA and mean DBH resulted higher o in Bia NP, followed by Ankasa, Bia and Da- e g dieso reserves; distributions were signifi- o i cantly different between Dadieso reserve B and both Ankasa and Bia NPs for trees with – DBH>20 cm, and between Dadieso and the t s other three areas for trees in the 10-20 cm e Fig. 2 - Recent above ground biomass (AGB) and basal area (BA) values. (ANP): r DBH range. The total number of trees per o Ankasa National Park; (DFR): Dadieso Forest Reserve; (BRR): Bia Resource Reserve;

F ha were: Ankasa 408; Dadieso 229; Bia re- i (BNP): Bia National Park. serve 368; Bia NP 352. The analysis of these structural parame- Fig. 3 - Changes in ters confirmed our first hypothesis (i) of basal area (BA), higher impacts on mean forest structure from past to values in the less protected areas where present values, selective logging or illegal disturbance oc- for trees with curred. DBH > 30 cm. (DFR): Dadieso Wood density and guild composition Forest Reserve; Mean WD analysis showed for both DBH (BRR): Bia ranges the highest in Ankasa and the low- Resource est in Bia NP; differences in distributions Reserve. were significant between Ankasa and both Bia NP and Dadieso, and between Dadieso and Bia NP. Percentages of trees per guild for DBH >20 cm showed that, from Ankasa to Bia NP, pioneer and non-pioneer light-deman- ding species increased, while shade-bearer species decreased (Fig. 1, Tab. S7 - Appen- dix 1); for the 10-20 cm DBH range the pat- tern was similar. Swamp species were too scarce for evaluation. The results from WD and guild composi- tion analysis are in disagreement with the first hypothesis (i) of stronger impacts in the less protected reserves. Instead, WD and guild composition followed the latitu- Fig. 4 - Changes dinal gradient. in guild composi- tion, from past to Temporal changes in basal area and present values, guild composition for trees with The BA from historical data totaled 15.96 DBH > 30 cm. m2 ha-1 in Dadieso and 23.82 m2 ha-1 in Bia (DFR): Dadieso reserves (Tab. S8 - Appendix 1). The BA Forest Reserve; change over time, tested for trees with (BRR): Bia DBH>30 cm to account for differences in Resource sampling thresholds in the surveys, was Reserve. not significant for any of the areas (Fig. 3). This is in contrast to what was defined in the second hypothesis (ii), namely a reco- very in BA for Bia NP, where logging activi- ties ceased two decades ago, and a de- crease for Dadieso, still affected by illegal activities. For Bia Reserve, the difference in BA from the neighboring Bia NP is more than 50%, revealing very degraded condi- tions; the even lower BA in Dadieso indi- cates a highly degraded forest. Historical guild data (Tab. S8 - Appendix 1) showed for that time a prevalence in Da- dieso (trees > 30 cm DBH) of non-pioneer

357 iForest 9: 354-362 Selective logging and illegal activities impact on forest resources in Ghana y

light-demanding species, followed by sha- r

Tab. 2 - Biodiversity indices calculated per area. (ANP): Ankasa National Park; (DFR): t

de-bearer, pioneer and swamp species; in s

Dadieso Forest Reserve; (BRR): Bia Resource Reserve; (BNP): Bia National Park. e

the 10-30 cm DBH range, the first two r guilds were instead inverted in order. In o F

Biodiversity indices Bia, for both DBH classes, the prevalent ANP DFR BRR BNP (DBH>10 cm) d guild was non-pioneer light-demanding, 2 n Area (m ) 17000 18400 8800 10000 a followed by shade bearer, pioneer, and Number of species 147 120 94 84 s swamp-resistant. e

# of individuals 841 402 290 335 c

Guild change over time, tested for trees n Singletons 54 48 39 37 e with DBH>30 cm (Fig. 4), was not signifi- i

Dubletons 22 22 17 7 c cant for any guild in Bia, contrary to the s Uniques 57 55 43 37 shift toward recovery predicted by our o Margalef’s richness 21.68 19.85 16.40 14.28 e third hypothesis (iii). For Dadieso, a signifi- Chao 1 ± st.dev. 213.19 ± 24.29 172.23 ± 20.09 138.58 ± 19.11 181.49 ± 49.83 g o cant increase in pioneer and shade-bearer Shannon’s Evenness 0.85 0.91 0.91 0.88 i B species was found, with significant decrea- – se of non-pioneer light-demanding species; t swamp-resistant trees were too limited for s

Fig. 5 - Carbon stock in e testing. The further Dadieso degradation the topsoil (0-30 cm) r o

predicted by our third hypothesis (iii) is in F

and subsoil (30-100 i contrast with the increase of shadow-bea- cm). Different letters rer species. indicate significant dif- Overall, Bia recovery has not occurred ferences for the 0-100 after two decades, with unaltered BA and cm depth. (ANP): guilds composition. In Dadieso BA condi- Ankasa National Park; tions are unchanged, while guild data only (DFR): Dadieso Forest partially support our initial hypothesis. Reserve; (BRR): Bia Resource Reserve; Species richness and biodiversity (BNP): Bia National The Margalef index (Tab. 2) indicated an Park. increasing species richness moving south- ward from Bia to Ankasa NPs. This result contradicts our first hypothesis (i) regar- ding a stronger impact in less protected areas. The richness distribution follows the latitudinal gradient as found for WD and guilds composition. The Chao 1 index (Tab. 2) was higher in NPs, and confirms our first hypothesis. For minimum sampling area, we obtained 3.02 and 3.27 ha respectively for Ankasa and Bia NPs, while for Bia and Soil carbon content rence between the two parks. In Dadieso Dadieso Reserves 1.28 and 3.06 ha, respec- Fig. 5 illustrates the SOC results. In the the C concentration was only significantly tively. We reported Shannon’s evenness in topsoil, the C concentration in the Bia lower than in Ankasa. Compared to the Tab. 2 and Coleman’s rarefaction curve in reserve was significantly lower (p<0.001) topsoil which stores roughly half of the Fig. S2 - Appendix 1. than in Ankasa and Bia NPs, with no diffe- total SOC stock stored down to 1 meter,

Tab. 3 - Seasonal Modified Kendall tests for NDVI and climate data from ten forested areas in Ghana. Z-test values, p-values and slopes are displayed. (FR): Forest Reserve; (NP): National Park; (RR): Resource Reserve. (NDVI): Normalized Difference Vegetation Index; (-): non significant; (*): p < 0.05; (**): p < 0.01; (***): p<0.001.

Variable Site Z p-value slope Variable Site Z p-value slope NDVI Ankasa NP 3.166 *** 0.003 Precipitation Ankasa NP 1.775 * 4.789 Subri FR 2.560 ** 0.002 Subri FR 2.424 ** 6.767 Tano FR 3.331 *** 0.003 Tano FR 2.079 ** 5.500 Kakum NP 2.515 ** 0.003 Kakum NP 2.268 ** 6.070 Subuma FR 3.966 *** 0.004 Subuma FR 2.247 ** 5.527 Dadieso FR 2.269 * 0.002 Dadieso FR 1.839 * 4.373 Anwhiaso FR 2.149 * 0.002 Anwhiaso FR 2.247 * 5.257 Krokosua NP 3.071 *** 0.002 Krokosua NP 2.223 * 4.091 Bia RR 2.054 * 0.002 Bia RR 2.176 * 3.572 Atewa FR 1.902 * 0.001 Atewa FR 0.778 - 1.928 Temperature Ankasa NP 2.515 ** 0.015 Cloud cover Ankasa NP 1.302 - - Subri FR 2.795 ** 0.016 Subri FR 1.174 - - Tano FR 2.286 * 0.014 Tano FR 1.739 * 0.261 Kakum NP 2.969 *** 0.017 Kakum NP 0.681 - 0.167 Subuma FR 2.412 ** 0.008 Subuma FR 1.227 - 0.250 Dadieso FR 2.321 ** 0.014 Dadieso FR 1.771 * 0.294 Anwhiaso FR 2.412 ** 0.013 Anwhiaso FR 1.227 - 0.250 Krokosua NP 2.234 * 0.013 Krokosua NP 1.834 * 0.366 Bia RR 2.331 ** 0.014 Bia RR 1.952 * 0.364 Atewa FR 2.545 ** 0.014 Atewa FR 0.417 - 0.048 iForest 9: 354-362 358 Vaglio Laurin G et al. - iForest 9: 354-362 y

r selective, promotes SOC decomposition by

t Tab. 4 - Spearman’s correlation values between NDVI time series and monthly climate

s creating gaps in the canopy cover and

e data for ten forested areas in Ghana. (NDVI): Normalized Difference Vegetation Index.

r reducing the inputs of organic C to soil o (Yanai et al. 2003, Li et al. 2015). All logging F Spearman’s correlation (ρ) activities leave behind large amounts of d Site n Cloud cover Precipitation Temperature slash which decomposes, and, combined a Ankasa NP -0.51 0.40 0.28 with an increased mortality of residual s e Subri FR -0.44 0.25 0.33 trees, increases ecosystem heterotrophic c Tano FR -0.44 0.48 0.31 n respiration and CO2 emissions for years fol-

e Kakum NP -0.43 0.32 0.24 i lowing harvest (Blanc et al. 2009, Huang & c Subuma FR -0.54 0.49 0.28 s Asner 2010). The topsoil, where most of Dadieso FR -0.52 0.48 0.31 o the tropical forests C is stored, is the layer e Anhwiaso FR -0.55 0.43 0.25 g Krokosua NP -0.57 0.42 0.28 more prone to changes following distur- o i Bia FR -0.60 0.40 0.19 bance, as observed by Chiti et al. (2014) in B Atewa FR -0.47 0.40 0.22 Western Ghana. Our results are not consis- – tent with what reported by Asase et al. t s (2012) for both Bia areas, as our Bia NP va- e r the C concentrations in the subsoil consi- lective logging in Bia. Even if we could col- lues are much higher. This discrepancy may o

F derably decreased: no detectable differen- lect only unofficial records, Hansen et al. be due to the low number of samples (4) i ces were observed among the sites. The (2012) evaluated that in Ghana the informal collected in the aforementioned study. significantly higher C stocks found in An- lumber sector, driven primarily by domestic For species diversity, results from the kasa and Bia NPs support the first hypothe- consumption, represents 80% of the mar- Chao 1 index indicated that the two NPs sis (I) of stronger impacts in less protected ket, and is six times the annual allowable have much higher species diversity than sites. cut. A balance between forest regrowth the reserves. This is agreement with the and persistent illegal activities, possibly results of a meta-analysis by Gibson et al. Remote sensing and meteorological coupled with natural disturbance, may ex- (2011), who showed that forest degrada- data plain the lack of BA change along time ob- tion has an overwhelmingly detrimental ef- NDVI trends over 30 years were analyzed. served for Dadieso. fect on tropical biodiversity; however, the Monthly averaged NDVI values in the ten The complete absence of recovery of BA authors reported that selecting logging forested areas remarked the two distinct in the Bia reserve might be explained if causes smaller impact compared to other productive seasons (Fig. S3 - Appendix 1). severe logging occurred after historical disturbance types. The area needed to Oscillations in 30-year NDVI temporal series data collection, possibly coupled with cli- sample diversity was higher in the NPs, showed consistency among the areas, but mate natural disturbance, which had been with the about 3 ha value being far from different magnitude (Fig. S4 - Appendix 1). reported for the drier Ghana tropical range the 1-1.5 ha commonly suggested as an ade- The Seasonal Modified Kendall tests (Tab. by Owusu & Waylen (2009). In this case quate sampling size (Alder & Synnott 1992, 3) indicated a significant NDVI increase for multiple stresses may have limited until Lanly 1981); such data could represent an all the areas, in accordance to our fourth now the ability of the forest to recover. indication for planning field diversity stu- hypothesis (iv) of a forest productivity in- Recovery is linked to the frequency of past dies in this region. crease along the last 30 years. Also precipi- disturbance events as well as their inten- According to our study, NPs were more tation (with the exception of Atewa FR, an sity and type, with some research estima- effective in conserving forest structure, upland rainforest) and temperature signifi- ting hundreds to a thousand years for a soil C content, and biodiversity. The even- cantly increased, while cloud cover data complete recovery of fragmented forests ness component of species diversity is es- showed a significant positive trend only for (Liebsch et al. 2008). Recovery from selec- pecially important for assessing the effects areas located in the most northwest range tive logging might be much longer than of degradation, as higher richness can be (Tano, Dadieso and Bia Reserves, and Kro- that estimated through growth models, as observed in disturbed areas as a result of kosua NP). Positive and negative NDVI fluc- previously observed in Ghana by Hawthor- an increased number of pioneer species. A tuations are more marked in areas closer ne et al. (2012), as forest biomass can con- reduced species evenness caused by distur- to the wet evergreen zone. tinue to decline after logging ceases due to bance represents alone an indication of Spearman’s ρ correlation was tested delayed effects on tree mortality (Blanc et biodiversity loss. This result adds to the exi- between NDVI time series and climate pa- al. 2009). The BA change observed in the sting evidence that selective logging has rameters (Tab. 4), with higher ρ values Bia reserve poses a warning for the sustai- severe impacts in African forests (Bedigian found using 12 months of precipitation, 5 nability of current selective logging sche- 1998, Hall et al. 2003, Hawthorne et al. of cloud cover (June-October), and 3 of mes. 2012). However, it is evident that this pro- temperature (July-September). For all but Bia NP had a significantly higher mean tection cannot be granted to all the forest one area (Tano FR) the highest correlation DBH and BA with respect to Ankasa NP; fragments of the region, while on the was found with cloud cover, with ρ values tree density was higher in Ankasa, but edges of NPs, such as Ankasa, economic ranging from -0.43 to -0.60, followed by larger stems were recorded in Bia. Moist problems are causing an increase in anthro- precipitation (ρ from 0.25 to 0.49) and tem- forests can have more favorable water pogenic pressure (Damnyag et al. 2013). In perature (ρ from 0.19 to 0.33). conditions, more nutrients and lower light order to avoid further depletion of forest compared with drier forests, resources within and around protected Discussion as observed by Hall & Swaine (1981) in Gha- areas, viable management solutions, such na, thus explaining the presence of such as the European Union FLEGT (Forest Law Degradation impacts and protection very large trees in Bia. Enforcement, Governance and Trade) faci- status With respect to SOC, the two NPs sho- lity should be strengthened (Ramcilovic- The C stocks and BA of Bia and Dadieso wed a C stock in the 0-100 cm depth similar Suominen et al. 2010), as well as smart and reserves were reduced by 50% or more or higher than the average reported for a cost-effective restoration approaches (Sa- compared to the Ankasa and Bia NP; signi- series of Oxisols from Western and Central saki et al. 2011). ficant reduction was also observed for Africa by Batjes (2008) and Henry et al. structural parameters (H, DBH). The illegal (2009). In contrast, results from the two Forest variables less influenced by activities reported for the Dadieso reserve reserves showed SOC stocks significantly disturbance had impacts similar or even worse than se- lower than these values. Logging, even if In this study arboreal richness distribu-

359 iForest 9: 354-362 Selective logging and illegal activities impact on forest resources in Ghana y

tion followed the latitudinal gradient. 2012, Graham et al. 2003, Pau et al. 2013). tiveness (Damnyag et al. 2013). It is urgent r t

According to the Intermediate Disturbance However, it is known that light availability to strengthen these approaches and pro- s e

Hypothesis, higher richness values are plays a key role in limiting photosynthesis perly value the range of services provided r found in moderately disturbed areas (Moli- (Mulkey et al. 1996). Seasonal productivity by forests, including agro-forestry systems o F no & Sabatier 2001). Bongers et al. (2009) changes have been already observed in the that could generate C credits (Norris et al. d found in Ghana that richness is less sensi- dry tropics, as a result of climate change, 2010). Selectively logged and degraded fo- n a tive to disturbance in wet than in dry fo- but usually are not investigated as often or rests remain critical for the conservation of s rests. Norris et al. (2010) in a West African intensely as long-term changes (Feng et al. biodiversity and C storage potential, once e c

review study observed that, although in 2013). The strongest negative correlation adequately protected (Lindsell & Klop n e some instances overall species richness in- of NDVI with cloud cover during the short 2013). Forest productivity increase is an op- i c

creased in disturbed sites, the richness of dry season, that was higher than that with portunity for recovery that is very impor- s endemic species tended to decline. Species precipitation, stresses the importance of tant to seize. This translates into the need o e richness is also known to increase with examining changes in seasonal patterns to reconsider the of selective g o rainfall amount in this region (Hall & Swai- beside long-term changes, especially in “bi- logging schemes, to improve controls in i B ne 1981). modal” seasonal forests. forests, to strengthen viable forest mana- – Functional composition, namely WD and gement alternatives, providing more incen- t guilds, was also distributed according to Forest resources conservation in a tives for conservation through internatio- s e the latitudinal gradient; the percentage of changing climate nal programs. r o

pioneer species, an indicator of disturban- In mostly undisturbed forests of Ghana, F ce, was not higher in the more degraded Fauset et al. (2012) found that prolonged Acknowledgments i areas. drought led to a consequent change in We acknowledge the EC FP7 ERC grant These results indicate that both richness functional composition toward drought-re- Africa GHG #247349. The funding source and functional composition are shaped by sistant species, with increase in pioneers was not involved in any part of the manu- climate parameters and less affected by and decrease in shadow-bearers. These script preparation. We are grateful to the the disturbance types we considered. shifts are similar to those caused by selec- Ghana Forestry Commission staff, to Jus- tive logging, which also increases pioneers tice Mensah, and to all the field collabora- Regional forest productivity and and decreases shadow-bearers through tors. We thank Prof. Ranga Mynani and Dr. climate variable trends gap openings. Unless there is certainty of Zeichuan Zhu for providing the GIMMS3g Previous studies showed the ability of absence of disturbance (including illegal dataset. G.V.L. thanks the EU for suppor- GIMMS3g to capture productivity dynamics activities), it is difficult to understand what ting the BACI project funded by the EU’s (Wang et al. 2014, Zhu & Southworth 2013). contributes more to a guild change. Our 692 Horizon 2020 Research and Innovation Our result of NDVI increasing trends is in historical data were collected in two dis- Programme under grant agreement 6401 agreement with the increase in carbon sto- turbed areas, but the lack of guild change 76. None of the authors has any conflict of rage observed for African intact tropical fo- found in the Bia reserve and the increase in interest. rests (Lewis et al. 2009), as well as with the shadow-bearer species observed in Dadie- increase in net primary production recor- so do not support a functional composition References ded at a global scale (Nemani et al. 2003). change in our study sites. Bongers et al. Alder D, Synnott TJ (1992). Permanent sample As most (7) of the ten sites we considered (2009) found that in Ghana the response in plot techniques for mixed tropical forest. Tropi- were reserves affected by multiple distur- guild composition to disturbance intensity cal Forest Papers 25: 124-145. [online] URL: bances, our results supports the view that changed according to forest type, being http://agris.fao.org/agris-search/search.do? productivity can be enhanced by fast gro- significant only in drier forests. It is possi- recordID=GB2012106028 wing species that are abundant in distur- ble that the forests located in the zone we Asase A, Asitoakor B, Ekpe P (2012). Linkages bed sites (Carreño-Rocabado et al. 2012). focused are more resilient to functional between tree diversity and carbon stocks in un- The magnitude of oscillations in NDVI de- changes. However, climate change is a con- logged and logged West African tropical fo- creased with increasing distance from the cerning issue in West Africa, with predic- rests. International Journal of Biodiversity Sci- wetter tropical forest type center (Fig. S4 - tions still uncertain, especially for precipita- ence Ecosystem Services Management 8: 37-41. Appendix 1), congruent with observations tion (Paeth et al. 2011). If considering cli- - doi: 10.1080/21513732.2012.707152 indicating that drier forests are characteri- mate alone, deciduous and evergreen fo- Batjes NH (2008). Mapping soil carbon stocks of zed by higher resiliency to changes and rests might expand in the future, but Central Africa using SOTER. Geoderma 146: 58- have 50-75% of the productivity of wetter human impacts will play a critical role in the 65. - doi: 10.1016/j.geoderma.2008.05.006 forest types (Ewel 1977, Murphy & Lugo future of forest resources (Heubes et al. Bedigian D (1998). of an African rain fo- 1986). 2011). New technologies such as laser scan- rest logging in Kibale and the conflict between The increasing trend in precipitation and ning for forests (Pirotti 2010, 2011, Santoro conservation and exploitation. Economic Bota- temperature resulting from 30 years of et al. 2013) will enable a better monitoring ny 52: 87-98. - doi: 10.1007/BF02861299 CRU 3.2 dataset analysis is opposite of the of changes to support decision and poli- Blake GR, Hartge KH (1986). Bulk density In: precipitation decrease observed by Fauset cies. Our results call for additional func- “Methods of soil analysis: Part 1. Physical and et al. (2012) and Owusu & Waylen (2009). tional change studies focused on the possi- mineralogical methods” (Klute A ed). Soil Sci- We explain this difference considering that ble varied responses of different forest ty- ence Society of America, American Society of the CRU datasets up to version 3.10.01 pes. Agronomy, Wisconsin, USA, pp. 363-375. were affected by a bug in precipitation Blanc L, Echard M, Herault B, Bonal D, Marcon E, data (http://www.cru.uea.ac.uk/cru/data/h Conclusions Chave J, Baraloto C (2009). Dynamics of above- rg/). Moreover, we examined a different Overall, the impacts observed in reserves ground carbon stocks in a selectively logged period (1981-2011) with respect to the afo- are alarming, especially considering that tropical forest. 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