Potential of dendrochronology to assess annual rates of biomass productivity in savanna trees of West Africa

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Original article

Potential of dendrochronology to assess annual rates of biomass productivity in savanna trees of West Africa Cheikh Mbow a,∗ , Sophan Chhin b , Bienvenu Sambou a , David Skole c a Institut des Sciences de l’Environnement (ISE), Laboratoire d’Enseignement et de Recherche en Géomatique (LERG), Faculté des Sciences et Techniques (FST), Université Cheikh Anta Diop de Dakar, BP 5005 Dakar Fann, Senegal b Department of Forestry, Michigan State University, 126 Natural Resources Building, East Lansing, MI 48824-1222, USA c Department of Forestry, Michigan State University, Global Observatory of Ecosystem Services (GOES), 101 Manly Miles Building, 1405 S. Harrison Rd., East Lansing, MI 48823, USA

a r t i c l e

i n f o

Article history: Received 13 October 2010 Accepted 6 June 2012 Keywords: Dendrochronology Biomass Carbon Savanna West Africa

a b s t r a c t We examined the potential of dendrochronology to assess biomass productivity of individual savanna species from a semi-arid ecosystem in southern Senegal. The 9 tree species examined in this dendrochronologial study included: Acacia macrostachya, Acacia seyal, Balanites aegyptiaca, Combretum glutinosum, Cordyla pinnata, Pterocarpus erinaceus, Terminalia macroptera, Daniellia oliveri, and Combretum nigricans. Dendrochronologial analyses were applied on cross-sectional disks obtained from the tree stem to reconstruct past tree growth (diameter and biomass) histories. Despite challenges with discerning annual tree rings in these savanna species (associated with ring suppression, wedging, indistinct ring boundaries, and fires), tree species (A. macrostachya, A. seyal, and T. macroptera) with the highest dendrochronology potential produced a clear thin band of marginal parenchyma. A. macrostachya had rapid annual diameter and biomass growth increments in the juvenile years (ages 1–10), compared to T. macroptera which showed greater growth past this early juvenile period. Given the same species, generally wetter forests had lower annual and cumulative growth rates that were likely due to increased inter-tree and tree-grass competition for soil moisture in the wetter forests. We concluded that dendrochronology is well suited for retrospective annual biomass assessment in savanna trees of Senegal, West Africa. © 2012 Istituto Italiano di Dendrocronologia. All rights reserved.

Introduction African savanna ecosystems are expected to play a significant role in climate change and carbon dynamics as are many other tropical ecosystems (IPCC, 2008). First, savanna systems can provide significant ecosystem services for poor and vulnerable rural populations that depend on products and environmental qualities of savanna trees (Mbow et al., 2008; Mertz et al., 2009). Second, the carbon sequestration potential for savanna landscapes has been demonstrated by several studies (Brown, 1997, 2002; San-Jose et al., 1998; Tiessen et al., 1998; Woomer et al., 2004). Senegalese ecosystems in particular are dominated by savanna woodlands, which are characterized by their continuous grass cover mixed with a range of tree types and densities (Aubréville, 1957). These ecosystems are very complex and include cover types from dense

∗ Corresponding author. Present address: ICRAF, World Agroforestry Centre, Research Unit GRP5, PO Box 30677-00100, Nairobi, Kenya. Tel.: +254 207224130. E-mail addresses: [email protected], [email protected], [email protected] (C. Mbow), [email protected] (B. Sambou), [email protected] (D. Skole).

dry forest to open shrubby cover types. The different types of savanna are discriminated by the proportion of woody and shrub crown cover (Adam, 1966). Despite differences in tree cover and grass composition (annual versus perennials), African savannas are burned almost every year, depending on the amount of herbaceous cover, tree type and composition, and moisture conditions (Nielsen and Rasmussen, 2001; Mbow et al., 2003). The biodiversity and function of savanna ecosystems are known to be extremely vulnerable to climate variability. To meet the technical needs for both climate adaptation and climate mitigation interventions, more technical research has been needed to assess biomass and carbon productivity of savanna woody vegetation (IPCC, 2006). In particular, information is needed on the impact of climate variability and change on savanna ecosystem productivity and the general trends of the biomass production associated with this variability and change. This requires knowing how fast individual species grow. This scientific challenge of assessing savanna tree productivity has been noted by Brown (1997). Attempts to document climate–biomass relationships using permanent plots or statistical models have produced few meaningful and useful results due to the lack of annual time series data over longer periods of the climate record. Data

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Please cite this article in press as: Mbow, C., et al., Potential of dendrochronology to assess annual rates of biomass productivity in savanna trees of West Africa. Dendrochronologia (2012), http://dx.doi.org/10.1016/j.dendro.2012.06.001

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from permanent plot studies is uncommon and difficult to use because individual studies often use different time steps and time frames (Metsaranta and Lieffers, 2009). Moreover, measurement precision is often variable between and within studies. Growth data from permanent plots also typically cover short time periods because field measurements for a sufficient number of samples is demanding work resulting in irregular and inconsistent time series. Further, when allometric models are used with field plot data to estimate biomass over time, the lack of location specific models and a lack of nested samples generate considerable uncertainty in obtaining accurate annual empirical relationships between climate records and biomass accumulation (Mbow, 2009). Production modeling approaches using biogeographical or biogeochemical models are of limited use in this case because exact parameterization of the physiological response to climate is imprecise due to lack of basic process-based data. At the landscape level, satellite imagery has been widely used by applying biomass proxies such as vegetation indices (Seaquist et al., 2003; Bronge, 2004; Budde et al., 2004; Li et al., 2004; Fensholt et al., 2009). Although it is straightforward to generate landscapewide vegetation index maps, it is more difficult to relate these indices with actual biomass and biomass increments on the ground (Diouf and Lambin, 2000; Tottrup and Rasmussen, 2004; Fensholt et al., 2006). Therefore, tree ring analysis can be an alternative approach to retrospectively estimate annual growth rates of individual species over large landscapes tied directly to climate records for the location. This is particularly important for understanding the coupling between climate and carbon, and in the case of monitoring carbon sequestration in location-specific interventions such analyses can be used to develop accurate models for estimating carbon storage from climate proxy data between inventories. This has important implications in developing countries that want to implement reforestation carbon offset projects over large areas when manpower for inventory measurements is lacking. It would provide a means to measure using standard methods on an infrequent basis (i.e., every 5 years) and use dendrochronology models for the intervening years. Moreover, dendrochronology-assisted measurements could be a useful and efficient approach for sample extrapolation across the landscape. To overcome these methodological limitations and lack of temporal and spatial precision inherent in plot and allometry approaches, dendrochronology has been tested in several ecosystems to derive wood productivity of individual species, impact of climate variables, growth model for given species and age of various stands including local ecological conditions of growth (Tarhule and Hughes, 2002). The formation of annual rings relates the annual growth characteristics of trees with climate seasonality characteristics of the location. In boreal and temperate regions, very distinct annual rings are induced by cambial dormancy during the winter period. In the tropical zone, tree rings are formed when trees experience cambial dormancy due to a long dry season in areas with distinct seasonality and during flood seasons in floodplain forest such as riparian ecosystems. Hence, in the tropical regions, cambial dormancy and tree ring formation is controlled by the seasonality in moisture availability (Rozendaal and Zuidema, 2010). Tree ring size can be related to annual biomass production using allometric models that relate tree biomass to tree diameter. Despite this high potential for dendrochronology approaches to understanding the relationship between carbon accumulation rates and climate, it has proven to be a somewhat challenging method for tropical trees (Worbes, 1995). In particular, climate fluctuations within seasons can cause formation of false or wedging rings (Worbes, 2002). As such, studies based on tree rings are rare in Africa (Stahle et al., 1999; Stahle, 1999;

Worbes and Junk, 1999; Couralet et al., 2005; Therrell et al., 2007; Gebrekirstos et al., 2008; Trouet et al., 2010) and relegated to a very few sites with a small number of species. This paper examines the potential for dendrochronological methods to retrospectively assess biomass production in individual savanna species. We analyze several African semi-arid savanna species including Acacia macrostachya, Acacia seyal, Balanites aegyptiaca, Combretum glutinosum, Cordyla pinnata, Pterocarpus erinaceus, Terminalia macroptera, Daniellia oliveri, and Combretum nigricans. These species were collected in five protected forests in the savanna ecosystems of Southern Senegal. Materials and methods This study was conducted at 5 protected forest sites in Senegal. At each site both standard tree measurements were made and destructive sampling was performed leading to a total of 101 trees that were harvested. The destructive sampling was used to measure actual tree biomass in stems, leaves and branches to derive allometric equations based on diameter at breast height and tree height (Mbow, 2009). For all harvested trees, we also cut stem disks for dendrochronological analysis. While the allometry data provided information about tree biomass, the tree ring analysis provided insights on growth dynamics and growth histories. This paper focuses on biomass assessment using dendrochronology. Study area The study area is a series of protected forests located in the southern part of Senegal (Fig. 1) in a semi-arid area with annual rainfall from 800 mm at its northern boundary to more than 1000 mm at its southern boundary. The protected forests have been selected following a climate (North-South) and human demographic (East-West) gradient. As in other parts of the Sahel, the area suffered from an extended drought from 1968 to 1982. Recent precipitation trends appear to be back to normal with some extreme precipitation events. The vegetation cover varies from dense to open savannas with various degrees of human pressures on vegetation including agriculture, urbanization, burning, and pastoralism. The protected forest areas selected for this study can be biogeographically stratified in the following way: (a) Welor, Ouli and Bala forests are in the North Soudanian zone, and (b) Patako and Kantora forests are in the Soudanian zone. The forests located in western Senegal (Welor and Patako) have the highest population forming the high-end of the human demographic gradient. These forest sites are within the Senegalese savanna ecological zone, which is characterized by mosaics of vegetation types (grassland interspersed with trees) with regular fires and strong human pressures. The study sites are arranged across a phytogeographic gradient, with a rainfall gradient from 606 mm/year to 827 mm/year derived from spatially interpolated data for the forest sites (Table 1). In semiarid and arid savanna regions, small differences in precipitation can have an impact on woody vegetation cover and net primary production (L’Hôte et al., 2002; Sankaran et al., 2005). In particular, a recent study by Hein et al. (2011) showed a non-linear relation between NPP and rainfall which revealed that small differences in rainfall can induce an appreciable difference in NPP. The main savanna vegetation cover types are dry savannas (includes shrub savanna and trees savanna), savanna woodland, and woodland (Table 1) (Aubréville, 1957; De Bie et al., 1998). Dry savannas are comprised of shrub and/or tree species where soil aridity is a major limiting factor for plant growth; furthermore, tree cover is usually between 25% and 50%. In contrast to dry savanna, savanna woodland have higher tree density because

Please cite this article in press as: Mbow, C., et al., Potential of dendrochronology to assess annual rates of biomass productivity in savanna trees of West Africa. Dendrochronologia (2012), http://dx.doi.org/10.1016/j.dendro.2012.06.001

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Fig. 1. Locations of sampled forests and climate zones of Senegal. The five sampled forests included Welor, Patako, Ouli, Bala, and Kantora.

Table 1 Key biogeographical characteristics of the sampled forests. Forest

Welor

Bala

Ouli

Patako

Kantora

Reference meteorological station Station latitude Station longitude Station mean annual temperature (◦ C) (min, max)a Station annual precipitation (mm) (SD)b Forest annual precipitation (mm) (SD)* Dominant vegetation cover Biogeographical zone Dominant soil types

Fatick

Tamba

Tamba

Kaolack

Velingara

14◦ 20 N 16◦ 26 W 28 (21, 35)

13◦ 46 N 13◦ 40 W 29 (22, 35)

13◦ 46 N 13◦ 40 W 29 (22, 35)

14◦ 8 N 16◦ 5 W 29 (21, 36)

13◦ 9 N 14◦ 7 W 28 (21, 36)

495 (125)

700 (160)

700 (160)

564 (131)

831 (150)

606 (126)a

645 (123)ab

699 (110)b

728 (138)b

827 (123)c

Dry savanna Soudan-Sahel Ferruginous lixiviated soils and clay

Savanna woodland Soudan Lateritic, stony soils

Savanna woodland Soudan Lateritic, stony soils

Savanna woodland Soudan Ferruginous non lixiviated soils and clay

Woodland Soudan Stony lateritic and ferruginous soils with clay

a

Min = minimum mean annual temperature and max = maximum mean annual temperature. Data from reference stations for the reference period of 1971–2000 and SD = standard deviation. * Data for each forest are based on spatially interpolated kriged data using all Senegalese stations for the reference period of 1971–2000; forests with different letters are significantly different at P < 0.05. b

of better rainfall and humidity conditions resulting in greater tree cover which is generally between 50% and 70%. Woodland is similar to savanna woodland, but has less understory grass cover because of a denser tree canopy cover around 75%. Table 1 indicates the location (latitude and longitude) of the nearest reference meteorological climate station for each forest site and associated geographical information. Average precipitation, derived from station annual precipitation data from 1971 to 2000 shows a large precipitation difference of more than 300 mm between the driest and wettest meteorological station. Precipitation is the most determinant ecological factor affecting woody vegetation in savannas in Africa whereas temperature and soil

parameters are often similar for the different forests (Breman and Kessler, 1995; Sankaran et al., 2005; Shorrocks, 2007; Good and Caylor, 2011). Field sampling A forest inventory was conducted between 2002 and 2004 in six protected forests (Welor, Patako, Ouli, Bala, Kantora and Mampaye). The method adopted for forest inventory was a stratified random sampling based on a preliminary individualization of homogenous covers corresponding to vegetation types using LANDSAT data from November 1999. Random selection from a

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Fig. 2. Leaf phenology and additional botanical characteristics of sampled species. Light gray boxes are the leafing periods from January (J) to December (D); Dark gray highlighted boxes are rainy season months; black crosses (×) are the vegetation types which the tree species are most frequently associated with. Source: Lebrun and Stork (1991–2010) and De Bie et al. (1998).

grid of 250 m × 250 m cells was overlaid onto the forests to locate sample plots of 20 m × 20 m (Sambou, 2004). Trees sampled for the dendrochronology analysis have been selected based on their abundance and distributed according to their frequency in class diameters. Accordingly, the most abundant species were the most sampled and most abundant diameter classes have more samples. The main observation for species selection is the dominance of C. glutinosum in each of the forests; and A. seyal was the dominant species only in Welor Forest. In total, 13 species have been collected for which a total of 101 trees were sampled for the development of allometric biomass equations (Mbow, 2009). A subset of these trees was used for the dendrochronological analysis reported in this study and consists of 62 trees spanning over 9 species (A. macrostachya, A. seyal, B. aegyptiaca, C. glutinosum, C. nigricans, C. pinnata, D. oliveri, P. erinaceus, and T. macroptera). The selection criteria for deriving this subset of trees were that only the most well-replicated tree species (3 or more trees) were retained for dendrochronological analysis. For each tree, a complete cross-sectional disk was sampled from the tree stem at stump height (0.3 m aboveground). For two large discs (>40 cm diameter) of P. erinaceus, half sections were collected but still included the central pith to allow for reconstruction of the complete tree growth history.

zone (ITCZ) (De Bie et al., 1998). All species are deciduous except D. oliveri which grows mostly in floodplain areas where soil moisture remains good throughout the year. Laboratory analysis Wood samples were processed according to standard dendrochronological techniques and sanded with progressively finer grades of sandpaper (up to 600 grit) to highlight annual rings (Stokes and Smiley, 1996). All wood samples were visually crossdated under a binocular microscope to identify any missing and/or false double rings by synchronously matching key narrow pointer years (Yamaguchi, 1991). The ring boundary for each year was examined and verified circumferentially between two radii of each disk. Furthermore, crossdating was conducted between multiple samples from the same site. The number of trees sampled within each forest for each species that were successfully crossdated is shown in Table 2. After verification of successful crossdating, wood samples were measured for annual ring width on a stage micrometer coupled with a binocular microscope to the nearest 0.001 mm (Velmex: Bloomfield, New York). Disk samples were measured along two radii from the pith to the outer ring of the year of sampling. Dendrochronological measurements

Species botanical characteristics of selected species A summary of species habitat and leaf phenology for each species sampled is shown in Fig. 2 (Lebrun and Stork, 1991–2010; De Bie et al., 1998). In addition to the prior descriptions of three vegetation cover types (i.e., dry savanna, savanna woodland, woodland): dry forest has a physiognomy of a closed canopy forest characterized by tree cover greater than 75%. Some species such as C. gutinosum have a wider ecological range than other species such as B. aegyptiaca. The species that are very well adapted to dry conditions are A. seyal, A. macrostachya and B. aegyptiaca; thus, they are generally found in dryer vegatation cover types (i.e., dry savanna, savanna woodland). Leaf phenology does not exactly overlap in time with the rainy season. Most species start leafing before the rainy season begins because of increased air humidity that prevails before the complete establishment of the intertropical convergence

All annual ring measurements for each radius of each crosssectional disk were constrained such that the cumulative growth of each radius had to sum to half of the diameter inside bark of each cross-sectional disk. This data correction method prevents over- or under-estimation of tree diameter derived from ring width measurements and was done using the following equation (Gebrekirstos et al., 2008): Correction factor =

DIB,R DIB,A

(1)

where DIB,A is the actual diameter inside bark of the crosssectional disk determined with a measurement tape and DIB,R is the maximum diameter inside bark determined from the cumulative ring width measurements. The correction factor had a mean value of 1.10 and varied between a minimum value of 0.76 and a

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Table 2 Dendrochronological potential of studied tree species sampled in each forest. Data is also summarized for all forests combined when a species is sampled from two or more forests. Number (percentage) of trees successfully crossdated

Mean age (minimum and maximum age range)

Fire scarsa

5 4 9

5 (100) 3 (75) 8 (89)

9.2 (7, 12) 16.7 (6, 25) 12.0 (6, 25)

4, 4 4, 3 8, 7

6 3

5 (83) 1 (33)

20.2 (18, 23) 24.0 (24, 24)

3, 3 1, 1

2 4 5 6 3 20 4

0 (0) 3 (75) 5 (100) 5 (83) 0 (0) 13 (65) 0 (0)

– 39.0 (24, 61) 21.0 (17, 26) 23.8 (20, 35) – 26.2 (17, 61) –

2, 0 4, 3 5, 5 6, 5 3, 0 20, 13 4, 0

Ouli Patako All Patako Patako

2 4 6 3 3

0 (0) 2 (50) 2 (33) 0 (0) 3 (100)

– 30.5 (20, 41) 30.5 (20, 41) – 22.0 (19, 28)

2, 0 4, 2 6, 2 0, 0 3, 3

Kantora Patako All

7 1 8

7 (100) 1 (100) 8 (100)

15.4 (7, 29) 15.0 (15, 15) 15.4 (7, 29)

6, 6 1, 1 7, 7

62

40 (65)

20.3 (6, 61)

52, 36

Species

Forest

Acacia macrostacya

Bala Ouli All

Acacia seyal Balanites aegyptiaca

Welor Welor

Combretum glutinosum

Bala Kantora Ouli Patako Welor All Patako

Combretum nigricans Cordyla pinnata

Daniellia oliveri Pterocarpus erinaceus Terminalia macroptera

Overall total

Total number of trees sampled

a

Fire scars listed as number of trees with presence of fire scarred tree rings relative to total number of trees sampled, followed by number of trees with fire scarred rings relative to number of successfully crossdated samples.

maximum value of 1.68. All annual ring width measurements were divided by this correction factor. At the end of each growth year, these corrected annual ring width measurements from each radius of a sample tree disk were converted to cumulative measures of diameter inside bark (DIB ). The next step involved determining the bark ratio (BR) for each disk sample from bark thickness measurements and then using the following equation (Bush and Brand, 2008):

BR =

DIB DOB

(2)

where DIB is the diameter inside bark and DOB is diameter outside bark. Cumulative DOB values were obtained by dividing cumulative DIB by BR; this conversion is required since allometric biomass equations require DOB as the input variable. Cumulative DOB values larger than 2.5 cm were then input in a composite allometric equation developed for savanna trees relating total above ground tree biomass (TAGB) to diameter at breast height (DBH , 1.3 m) (Mbow, 2009): 2 3 ) + 0.008(DBH ); TAGB (kg) = −0.463(DBH ) + 0.356(DBH

n = 101, r 2 = 0.85

(3)

A species-specific allometric equation was not used because there was an insufficient sample size for many of the tree species to generate species-specific equations (Mbow, 2009). Annual diameter increment (cm/year) and annual stem biomass increment values (kg/year) were obtained by subtracting cumulative growth in the previous year (t − 1) from the current year (t). The annual stem diameter (cm/year) and annual stem biomass increment values (kg/year) derived from the number of radii sampled from each disk were averaged together.

Results Tree-ring formation patterns The wood anatomy of the studied species is different from those of temperate and boreal species, as is often the case of tropical species. The distinctiveness of the ring boundaries in these tropical savanna species is often hindered by a alternation of parenchyma bands and fiber tissues produced within one growing season (Fig. 3). Tree ring boundaries of the following species were demarcated mainly by a thin marginal band of parenchyma: A. macrostachya, A. seyal, C. pinnata and T. macroptera (Fig. 3a, b, f, and i). A thin band of marginal parenchyma in combination with an accumulation of large diameter vessels produced early in the growing season after cambial reactivation was characteristic of B. aegyptiaca, C. glutinosum, and C. nigricans (Fig. 3c–e). In the samples of D. oliveri, ring boundaries were separated by thick bands of parenchyma (Fig. 3g). Tree ring boundaries in samples of P. erinaceus and C. pinnata were generally demarcated by alternating bands of parenchyma (light colored xylem tissue) and fiber tissue (darker colored xylem tissue), with the thickness of each type of band getting slightly smaller toward the end of the growing season (Fig. 3h). The tree-ring analysis can be straightforward for some species but for several samples, the analysis was impossible because of growth suppression and wedging. This explains the low percentage of successfully dated trees of about 65% (cf. Table 2). Fire scars were also prevalent in many of the cross-sectional disks. Mean annual growth increments Analysis of annual growth variables is useful to rank the species according to their ability to grow fast and accumulate biomass (Tables 3 and 4). Growth of three species (B. aegyptiaca, C. nigricans, and D. oliveri) could not be measured because either none or only one of their trees could be successfully crossdated and because uncertainty persisted on their annual character (cf. Table 2). In

Please cite this article in press as: Mbow, C., et al., Potential of dendrochronology to assess annual rates of biomass productivity in savanna trees of West Africa. Dendrochronologia (2012), http://dx.doi.org/10.1016/j.dendro.2012.06.001

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Fig. 3. Images of the transverse surface of wood samples of nine savanna tree species showing annual tree rings produced under a unimodal precipitation regime: Acacia macrostachya (a), Acacia seyal (b), Balanites aegyptiaca (c), Combretum glutinosum (d), Combretum nigricans (e), Cordyla pinnata (f), Daniellia oliveri (g), Pterocarpus erinaceus (h), and Terminalia macroptera (i). The boundaries for 1 year of growth are indicated by the tip of the black arrows. The direction of the arrows indicates the direction of growth from the start to the end of a growing season. The scale bar in the top-right corner of each sub-figure is equivalent to a length of 1 mm.

terms of mean annual diameter increment, the most rapid growing trees are A. macrostachya and T. macroptera (Table 3). It appears also that biomass production depends on how large the tree gets during its growth period examined in this study. While A. macrostachya grows very rapidly during its early years, it does not obtain the highest biomass of all trees. In contrast, for T. macroptera, A. seyal and P. erinaceus, they had increased annual biomass growth rates as they grew to a larger size and age (entire growth period (age 1–end) versus juvenile period (age 1–10)) (Table 4). Mean annual diameter and biomass increment for A. macrostachya is higher in Bala forest than in Ouli (Tables 3 and 4). This pattern for A. macrostachya is consistent for its entire growth

period (1–end) and for its juvenile period (1–10 year). For C. glutinosum, the mean annual diameter and biomass increment and for all age periods considered is highest for Ouli forest compared to the other forests (Patako and Kantora); of these forests, Ouli forest is generally drier than Patako which in turn is drier than Kantora (cf., Table 1). Cumulative growth trajectories The cumulative diameter growth trajectory at age 5 indicated that A. macrostachya generally grew the fastest followed by A. seyal and T. macroptera. In contrast, C. glutinosum and

Table 3 Descriptive statistics of mean annual diameter increment summarized by tree species and for all forests combined (a) and for tree species and individual forests for which there was sufficient sample replication (n > 2) (b). Mean end age

Age period: 1–enda Mean, cm (n)

Age period: 1–10b Mean, cm (n)

Age period: 1–20c Mean, cm (n)

(a) Species and all forests combined Bala Ouli Acacia macrostachya Welor Acacia seyal Combretum glutinosum Kantora Ouli Patako Patako Cordyla pinnata Patako Pterocarpus erinaceus Kantora Patako Terminalia macroptera

16.7 20.2 26.2 30.5 22.0 15.4

0.98 (8) 0.62 (5) 0.54 (13) 0.43 (2) 0.60 (3) 0.85 (8)

1.03 (7) 0.74 (5) 0.55 (13) 0.41 (2) 0.40 (3) 0.91 (8)

0.50 (11) 0.44 (2) 0.58 (3) 0.81 (3)

(b) Species and Individual Forests Acacia macrostachya Acacia macrostachya Combretum glutinosum Combretum glutinosum Combretum glutinosum

9.2 16.7 39.0 21.0 23.8

1.09 (5) 0.80 (3) 0.39 (3) 0.66 (5) 0.50 (5)

1.12 (2) 0.88 (2) 0.32 (3) 0.69 (5) 0.54 (5)

0.31 (3) 0.63 (4) 0.50 (5)

Species

a b c

Forest

Bala Ouli Kantora Ouli Patako

Age period: 1–end refers to the time period from age one up to the entire period of averaged growth. Age period: 1–10 refers to the time period from age one up to age 10 of averaged growth. Age period: 1–20 refers to the time period from age one up to age 20 of averaged growth.

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Table 4 Descriptive statistics of mean annual biomass increments summarized by tree species and for all forests combined (a) and for tree species and individual forests for which there was sufficient sample replication (n > 2) (b). Biomass was determined from a composite allometric equation relating tree biomass as a function of tree diameter. Mean end age

Age period: 1–enda Mean, kg (n)

Age period: 1–10b Mean, kg (n)

a) Species and all forests combined Bala Ouli Acacia macrostachya Welor Acacia seyal Combretum glutinosum Kantora Ouli Patako Cordyla pinnata Patako Patako Pterocarpus erinaceus Kantora Patako Terminalia macroptera

16.7 20.2 26.2 30.5 22.0 15.4

3.64 (8) 3.81 (5) 3.26 (13) 2.35 (2) 3.71 (3) 4.73 (8)

3.57 (7) 2.79 (5) 1.43 (13) 0.48 (2) 0.51 (3) 2.97 (8)

(b) Species and individual forests Acacia macrostachya Acacia macrostachya Combretum glutinosum Combretum glutinosum Combretum glutinosum

9.2 16.7 39.0 21.0 23.8

4.08 (5) 2.91 (3) 2.88 (3) 4.11 (5) 2.62 (5)

4.11 (2) 2.67 (2) 0.26 (3) 2.33 (5) 1.23 (5)

Species

a b c

Forest

Bala Ouli Kantora Ouli Patako

Age period: 1–20c Mean, kg (n)

2.37 (11) 1.44 (2) 2.75 (3) (3)

0.95 (2) 0.65 (3) 3.66 (4) 2.10 (5)

Age period: 1–end refers to the time period from age one up to the entire period of averaged growth. Age period: 1–10 refers to the time period from age one up to age 10 of averaged growth. Age period: 1–20 refers to the time period from age one up to age 20 of averaged growth.

Fig. 4. Cumulative patterns in diameter outside bark summarized by tree species for all forests combined (a) and for tree species and individual forests for which there was sufficient sample replication (b). All cumulative curves were truncated when the number of trees was less than three.

P. erinaceous grew slower than the previously mentioned three species (Fig. 4a). By age 10, cumulative diameter growth of T. macroptera outpaced cumulative growth of A. macrostachya and A. seyal, while growth of C. glutinosum and P. erinaceous continued to be relatively lower than the other species. By age 20, T. macroptera had the greatest diameter growth followed by A. seyal and C. glutinosum.

There was sufficient tree replication to compare cumulative diameter growth of A. macrostachya and C. glutinosum between the different sampled forests (Fig. 4b). Despite generally higher precipitation conditions in Ouli forest compared to Bala forest (spatially interpolated precipitation values in Table 1), cumulative diameter growth of A. macrostachya in Bala forest was greater than that in Ouli forest at any common overlapping age period. Similarly, and

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Fig. 5. Cumulative patterns in biomass summarized by tree species and for all forests combined (a) and for tree species and individual forests for which there was sufficient sample replication (b). All cumulative curves were truncated when the number of trees was less than three.

up to age 17, cumulative diameter growth of C. glutinosum was greatest in Ouli forest followed by decreasing growth in Patako and Kantora forest, despite that Kantora forest generally had the highest precipitation patterns (Table 1). Cumulative biomass growth trajectories relative to tree age (Fig. 5a) showed similar patterns as previously described for cumulative diameter growth trajectories (cf. Fig. 4a). From age 10 and later, T. macroptera continued to have the highest cumulative biomass growth rate compared to the other tree species. There was only enough tree replication and sufficiently large diameters that exceeded the minimum diameter threshold of the allometric biomass equation to compare biomass dynamics for C. glutinosum between the different forests (Fig. 5b). The relative patterns in cumulative biomass growth patterns mirrored that of cumulative diameter growth (cf. Fig. 4b) in that biomass patterns up to age 17 was greater in Ouli forest compared to decreased growth in Patako forest, and lowest growth in Kantora forest. Discussion Dendrochronological potential There were some difficulties in discerning tree-ring boundaries due to the wide range of wood anatomy and density variation produced during the growing season. The work of Worbes (1995) points out that annual rings are sharper under a unimodal

rainfall regime since a unimodal precipitation regime has a longer dry period between the rainy season of 1 year and the rainy season of the following year. In tropical regions of Africa, tree rings are produced corresponding to the number of wet seasons which in turn is related to the number of passages of the ITCZ (Shorrocks, 2007). Regions with only a single passage of ITCZ produces only one wet season (unimodal precipitation pattern) which leads to one annual ring produced in a calendar year. The main problem in discerning tree rings for savanna species is mainly associated with ring suppression, wedging, indistinct ring boundaries, and (or) fires. Some problems related to injuries on trunks were reported by Worbes (1995) and Tarhule and Hughes (2002). With the impact of humans (including brush fires carried predominantly via grasses), some rings were not distinct. Despite these difficulties, we generally found that species (families) with a clear band of thin marignal parenchyma, like there are in A. macrostachya (Mimosaceae), A. seyal (Mimosaceae), and T. macroptera (Combretaceae), were the easiest to successfully crossdate; C. pinnata is an exception to this rule. In Niger, Nicolini et al. (2010) also had success with ring-boundary distinctiveness of A. seyal which was delimited by marginal parenchyma. In their study, the maximal ages of the two site chronologies of A. seyal were 15 and 20 years which is comparable to the maximal age of 23 years for A. seyal in the current study in Senegal. Studies in West Africa by Tarhule and Hughes (2002), show that the potential of tree-ring analysis for tree growth give varying

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performances depending upon tree species. These authors have identified three categories of species in terms of their potential for dendrochronology: (1) potentially useful, (2) problematic, and (3) poor. The categorization is based on the distinctiveness of the annual ring boundaries, the ability for crossdating, ring circuit uniformity, ring wedging, and ring width variability. Those with which one can expect to have good results (i.e., potentially useful) include some of the species considered in our study such as C. pinnata, D. oliveri, and A. seyal. Our results showed nevertheless that D. oliveri and C. pinnata are very difficult to analyze for dendrochronology purposes. Tarhule and Hughes (2002) identify a list of species where tree-ring boundary detection is difficult and not useful for dendrochronology (i.e., problematic and poor). In this group, we notice C. glutinosum, C. nigricans, T. macroptera, A. macrostachya, P. erinaceus, Sterculia setigera and B. aegyptiaca. We note however, through the analysis done in this work that some of the species considered by Tarhule and Hughes (2002) as not useful for treering analysis, have a real potential for dating and analysis of growth rings: these are C. glutinosum, T. macroptera, A. macrostachya and P. erinaceus. The difference between our results and others may be related to the nature of the study sites (e.g., fire frequency) and differences in the character of their rainfall seasons. Some additional issues can be raised based on the method used. Some authors have explored obtaining samples in the form of increment cores (e.g., Maingi, 2006), while others generally prefer discs (Worbes, 2002). In tropical regions, true ring boundaries corresponding to seasonal dry periods, generally span the entire circumference of the tree (Worbes, 2002). In contrast, false ring boundaries may be produced in the middle of the season of ring formation because of episodic drought (Kozlowski et al., 1991) or may potentially be related to ring anatomy patterns unique to each tree. A sample taken with an increment borer is too small to distinguish between complete rings and wedging rings (personal observation). Furthermore, tropical tree species have a tendency during stressful growth periods to only produce rings on one side of the stem while no rings are formed on other sides (phenomena known as wedging) (Worbes, 2002). While it is nevertheless proven that discs provide more information, obtaining such disks requires destructive sampling of the tree. Role of tree-growth phenology Another common difficulty associated with tropical dendrochronology is phenological asynchrony of species (Worbes, 2002). Some species lose their leaves prematurely while others hold their leaves for a long time (Fig. 2). The duration of leaf-on conditions determines the dynamics of photosynthesis and in turn the magnitude of biomass production within a growing season. In the species analyzed here, leafing periods often begin weeks before the rainy season starts, and are triggered by increasing air humidity (De Bie et al., 1998). The evergreen species that we considered (D. oliveri) did not have a pronounced dormancy period as the other deciduous species. This appears to be a factor in the lack of successful crossdating in D. oliveri due to the formation of indistinct tree-ring boundaries which were partly obscured by the thick bands of parenchyma in the ring-boundary regions. The work of Chhin et al. (2008) also suggests that boreal tree ring growth depends on the process of leaf production and seasonal precipitation patterns before the current growing season of ring formation. The faster growing tree species is T. macroptera which has a great affinity for floodplain margins and deep soils. P. erinaceus seems to suffer from fire and drought in the first years but diameter growth recovers well after year 10 when root systems enable better resistance to drought and competition. A precise interpretation of tree rings in savanna ecosystems will therefore require a more

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direct consideration of joint analysis of climatic and phenological growth patterns on tree diameter growth (e.g., band dendrometers) (Worbes, 1995). Potential role of competition, fire and drought This paper shows both the complexity of the tropical tree-ring analysis and the potential of this method for tree growth history and dating of some species. Tree growth in savannas is strongly associated with many exogenous and edaphic factors such as bushfires, the extent of drought, competition between species, soil type and rooting systems of trees (Sankaran et al., 2005; Shorrocks, 2007; Good and Caylor, 2011). The complex influence of ecological, site, climate, and anthropogenic factors on tree growth makes it difficult to develop general models or theories of savanna woody productivity. The growth pattern of some individuals sometimes shows a contradiction with certain ecological theories which suggest that the seedlings grow faster than adults. Like many other plants, the growth curve of trees generally follows an S-shaped logistic curve in which the juvenile period is generally characterized by fast growth (i.e., the exponential phase of a logistic growth curve) (Silvertown and Charlesworth, 2001). After this rapid growth period, these trees may grow appreciably slower as they mature (i.e., reaching the upper limit of a logistic growth curve). The effect of favorable environmental and site factors (rainfall and soil fertility) can be reduced or minimized by periodic and time-dependent stress factors such as prolonged drought, periodic fires, high temperature, soil leaching, and other situations. We would expect that the wettest forests would have the highest production since tree growth rates are generally positively associated with increased precipitation which is the main limiting abiotic factor in savanna ecosystem (Shorrocks, 2007). However, in the case of C. glutinosum, production is slower in the wetter forest sites (Kantora) compared to the dryer forest sites (Patoka and Ouli). In other words, given the same species, generally wetter forests had lower annual and cumulative growth rates. We postulate this is likely due to increased inter-tree and tree-grass competition for soil moisture in the wetter forests. Other ecological factors and overall climate variability may appear to be influencing growth more than moisture. More analysis on environmental, edaphic and external controls on ring growth are required to explain such patterns (Schongart et al., 2006). It is worth noting that species adaptation to dry conditions is important to consider in this context. Some slow growing trees are most adapted to extreme edaphic conditions. For instance, Acacia species are generally known for developing deep extensive root systems (De Bie et al., 1998). Species such as C. glutinosum are so adapted and common to these savanna ecosystems that promotion of biomass production should not overlook this tree species. Other species are slowly growing but provide numerous ecosystem services; for example, C. pinnata is commonly used in agroforestry. The other important factor to consider is the species abundance in the landscape. Species such as A. macrostachya and C. glutinosum can become easily dominant in some sites resulting in higher tree density due to their adaptation to drought conditions. Conclusion and perspectives Linkage to carbon accounting and carbon markets The development of landscape models of biomass accumulation using dendrochronology has important implications for carbon measurement systems, particularly for reforestation interventions in the context of climate mitigation. Carbon accounting

Please cite this article in press as: Mbow, C., et al., Potential of dendrochronology to assess annual rates of biomass productivity in savanna trees of West Africa. Dendrochronologia (2012), http://dx.doi.org/10.1016/j.dendro.2012.06.001

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in reforestation offset projects often involve considerable effort to measure sample plots and can greatly increase the cost of reporting. For developing countries this can be an obstacle to participation in carbon markets due to a lack of trained personnel. Furthermore, since trees generally grow slowly, most measurements are reported over a five-year basis. The certification period for most carbon offset projects begins after 5 years. This prevents projects from receiving tradable income from carbon sequestration before the five-year certification window. On the other hand, the development of models that tightly link meteorological proxy data for the landscape to biomass accumulation through dendrochronology could permit more frequent reporting of carbon offsets. One caveat from this study is that meteorological variables alone are not pure proxies for biomass and growth rates. Site conditions, disturbance events, tree density and other factors also influence growth, and should be taken into account. Nonetheless there is considerably tight coupling between measured environmental data such as precipitation and temperature, that dendrochronology could be useful for developing estimates of carbon sequestration in projects on the landscape. Most carbon measurement methods for reforestation projects require the use of allometric equations or yield models to estimate carbon stocks and the change in carbon stocks over time. Throughout the tropics permanent plots with a long history of measurements are scarce, and considerable time would be required to obtain useful allometric data from newly established plots. This is a serious data limitation for the use of IPCC (2006) Tier 3 allometric equations (i.e., allometric models based on locally developed data and parameters from a forest stand inventory) in tropical landscapes. On the other hand a dendrochronological model allows for a rapid development of allometry for extant landscapes by using the historical reconstruction of biomass accumulation via ring analysis by using tree diameter as proxy of yield. Thus the use of dendrochronology to develop new allometry or calibrate general allometry, such as IPCC (2006) Tier 1 (i.e., allometric models based upon global IPCC default values and parameters), is a promising approach to overcoming limitations in tropical environments. Future research A potential new area of research could be developed to relate dendrochronological analyses with climate pattern analysis on a landscape scale. The focus of this approach would be to reconstruct climate in conjunction with carbon, biomass, or yields by establishing climate–growth relationships (Rozendaal and Zuidema, 2010). This new orientation of dendrochronology may supplement the traditional tree age or reconstruction of disturbance history in tropical forests. Currently, there have been several attempts to use dendrochronology for net primary productivity (NPP) assessment through biomass productivity estimation from tree ring analysis of growth rates (e.g., Misson, 2004). Such studies are based on applying tree ring data to growth models or replacing diameter growth measures with ring width in experimental plots. The possibility to track back the DOB (diameter outside bark) of individual trees makes it possible to apply allometric models at various ring sizes to estimate dated biomass during the tree life time. This information can be valuable to construct tree population models for sustainable forest management (Rozendaal and Zuidema, 2010). Acknowledgements The funding for the study was provided by SUN – tools for management and sustainable use of natural vegetation in West Africa (EU FP7 031685). START-PACOM (Grants for Global Environmental

Change Research in Africa). Many thanks to IFS who provided the initial funding, and to M. Dieng, E. David, and M. Magruder who provided valuable assistance with dendrochronological data collection. We also thank D. MacFarlane for helpful statistical advice. Thanks to Michigan State University (MSU), Department of Forestry and Global Observatory for Ecosystem Services (GOES) for laboratory support. We thank the associate editor and two anonymous reviewers for their insightful comments.

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