1 MANGROVES FACING CLIMATE CHANGE: LANDWARD MIGRATION POTENTIAL IN 2 RESPONSE TO PROJECTED SCENARIOS OF SEA LEVEL RISE. 3 4 Di Nitto D.1*, G. Neukermans1, N. Koedam1, H. Defever1, F. Pattyn3,4, J.G. Kairo5 and 5 F. Dahdouh-Guebas1,2 6 7 1 Biocomplexity Research Focus c/o Laboratory of Plant Biology and Nature Management, Mangrove 8 Management Group, Vrije Universiteit Brussel - VUB, Pleinlaan 2, B-1050 Brussels, Belgium. 9 2 Laboratoire d'Écologie des Systèmes et Gestion des Ressources, Département de Biologie des Organismes, 10 Faculté des Sciences, Université Libre de Bruxelles - ULB, CP 169,Avenue F.D. Roosevelt 50, B-1050 11 Bruxelles, Belgium. 12 3 Laboratory of Physical Geography, Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussels, Belgium. 13 4 Unité de Recherche Sciences de la Terre,Université libre de Bruxelles, Brussels, Belgium. 14 5 Kenya Marine and Fisheries Research Institute, PO Box 81651, Mombasa, Kenya. 15 16 * Corresponding author: diana.dinitto@gmail.com 1 2 ABSTRACT 3 Mangrove forests prominently occupy an intertidal boundary position where the 4 effects of sea level rise will be fast and well visible. This study in East Africa (Gazi 5 Bay, Kenya) addresses the question whether mangroves can be resilient to a rise in 6 sea level by focusing on their potential to migrate towards landwards areas. The 7 combinatory analysis between remote sensing, DGPS-based ground truth and 8 digital terrain models (DTM) unveils how real vegetation assemblages can shift 9 under different projected [minimum (+9cm), relative (+20cm), average (+48cm) and 10 maximum (+88cm)] scenarios of sea level rise (SLR). Under SLR scenarios up to 11 48 cm by the year 2100, the landward extension remarkably implies an area 12 increase for each of the dominant mangrove assemblages, except for Avicennia 13 marina and Ceriops tagal, both on the landward side. On one hand, the increase of 14 most species in the first 3 scenarios, including the socio-economically most 15 important species in this area, Rhizophora mucronata and C. tagal on the seaward 16 side, strongly depends on the colonisation rate of these species. On the other hand, 17 a SLR scenario of +88 cm by the year 2100 indicates that the area flooded only by 18 equinoctial tides strongly decreases due to the topographical settings at the edge of 19 the 20 assemblages will further decrease as a formation if they fail to adapt to a more 21 frequent inundation. The topography is site-specific; however non-invadable areas 22 can be typical for many mangrove settings. 23 inhabited area. Consequently, the landward Avicennia-dominated 1 Keywords: Sea Level Rise – Mangroves – Topography – DTM - Gazi Bay – 2 Inundation – Landward migration – GIS 3 1 2 1. INTRODUCTION 3 4 Inhabiting the interface between land and sea, mangroves are amongst one of the 5 most at-risk ecosystems when sea level rises (McLeod and Salm 2006). 6 Throughout the Quaternary, mangroves have shown high resilience to disruptions 7 from large sea level fluctuations over historic time scales (Woodroffe 1990). 8 However, adaptation probabilities strongly depend on the rates of SLR and 9 sediment supplies in combination with subsurface processes that affect sediment 10 elevation (Gilman E. et al. 2007, Gilman E. L. et al. 2006, McLeod and Salm 2006, 11 Wolanski and Chappell 1996, Woodroffe 1990). 12 suggested that mangroves are stressed by SLRs between 9 and 12 cm over 100 13 years and concluded that faster rates could seriously threaten mangrove 14 ecosystems. This view has been challenged by Snedaker et al. (1994) who cite 15 historical records showing mangrove expansion under relative sea level changes 16 nearly twice that high, however hard scientific data or SLR simulations are not 17 available. 18 As mangrove ecosystems are very dynamic, the ability of these forests to migrate 19 to more landward zones is a very important aspect when considering the effect of 20 SLR on mangroves. If the possibility poses, mangroves will adjust to a SLR by 21 expanding landward or laterally into areas of higher elevation, or even by growing 22 upward in place (McLeod and Salm 2006). However, mangroves areas situated in 23 a physiographic setting that limits landward migration due to obstacles or steep Ellison and Stoddart (1991) 1 gradients and with a net decrease in sediment elevation or sediment accretion that 2 is insufficient to keep up with SLR, are most vulnerable (Gilman E. L. et al. 2008). 3 On a species level, adaptation can occur through landward migration at different 4 speeds as mangrove species maintain their preferred hydroperiod or by sediment 5 accretion (Gilman E. L. et al. 2008). Mangrove species composition can strongly 6 affect mangrove’s resistance and resilience to SLR given that on the one hand 7 individual species have varying tolerances of the period, frequency, and depth of 8 inundation, and on the other hand different vegetation zones have different rates 9 of change in sedimentation elevation (Krauss et al. 2003, McKee et al. 2007, 10 Rogers et al. 2005). Furthermore, several scientists have also investigated how 11 different functional root types of several mangrove species respond to changes in 12 elevation in order to determine the vulnerability to SLR (Ellison and Stoddart 13 1991, Vincente 1989). 14 Species-specific competition may allow some species to outcompete others and to 15 become more dominant within the newly formed species composition (Lovelock and 16 Ellison 2007). Establishment and dispersal play a significant role in these 17 processes. They are however different for various species and strongly dependent 18 on many biotic factors like buoyancy, period of obligate dispersal, longevity and 19 period of establishment (Allen and Krauss 2006, Clarke et al. 2001, Drexler 2001, 20 Tomlinson 1986), whilst wind and hydrodynamics of tides and currents can be 21 equally important abiotic factors (Stieglitz and Ridd 2001). Additionally, factors 22 like microtopography, top soil type and root structures can also have a significant 23 effect on the fate of propagules once released from their parental tree, as can 24 human induced degradation, like tree cutting (Di Nitto et al. 2008). 1 2 To date, mangroves have been subjected to non-climate related anthropogenic 3 stressors which have accounted for most of the global average annual rate of 4 mangrove loss, estimated to be 1-2%, with losses during the last quarter century 5 ranging from 35 to 86% (Alongi 2002, Duke et al. 2007, FAO 2003, 2007, Valiela et 6 al. 2001). So far, relative SLR has been a smaller threat to mangroves. However, 7 it may constitute a substantial proportion of predicted losses (about 10-20% of total 8 estimated losses) as several studies have already shown that many mangrove 9 areas have not been keeping pace with current rates of relative SLR (Cahoon et al. 10 2006, Gilman E. et al. 2007, McKee et al. 2007). We would like to emphasize the 11 importance of understanding mangrove responses to SLR as these ecosystems 12 provide tremendous social, economic and ecological value (Barbier 2003, Dahdouh- 13 Guebas F. et al. 2005, Mumby et al. 2004, Nagelkerken et al. 2008, Walters et al. 14 2008, Wells et al. 2006). 15 16 This study focuses on the critical factor ‘tidal range’ in order to investigate the 17 potential for landward migration of mangrove vegetation assemblages in Gazi Bay 18 (Kenya) under different SLR scenarios. As mangroves species have their preferred 19 hydroperiod, the vegetation distribution in the different inundation classes at 20 present is extrapolated towards future SLR scenarios based on a static mangrove 21 surface elevation. Digital terrain modelling is derived from differential GPS field 22 measurements and used to simulate water levels in a GIS environment. 23 combination with a mangrove species map, preliminary results are generated 24 regarding the effect of SLR in the study site in Gazi Bay (Kenya). The focus In 1 resides on individual mangrove species and their possible colonization of back- 2 mangrove areas that become accessible when sea level rises. 3 adopt a reductionistic approach by taking abstraction of alterations in 4 sedimentation and elevation and to other consequences of global change such as 5 increases in temperature, CO2 concentration and storm frequency and possible 6 shifts in seasonal periods (Pernetta 1993, UNEP 1994, Woodroffe 1990, Woodroffe 7 and Grime 1999). However we feel that, in this context, relevant conclusions can 8 be made. First of all, this study represents the first attempt to simulate the effect 9 of SLR based on a large amount of detailed information on topography and We deliberately 10 vegetation covering the whole bay. 11 gathered valuable information within this study area on diverse subjects like 12 regeneration, vegetation structure dynamics, human impacts and 13 dispersal (e.g. Abuodha and Kairo 2001, Bosire J. O. et al. 2003, Bosire J. O. et al. 14 2008b, Dahdouh-Guebas Farid and Koedam 2006, Dahdouh-Guebas F. et al. 15 2002a, Di Nitto et al. 2008, Kairo J. G. et al. 2001, Kirui et al. 2008, Neukermans 16 et al. 2008). The latter gives us the opportunity to draw preliminary conclusions on 17 the potential for landward migration of mangroves in Gazi Bay and to create some 18 views on the future vegetation structure dynamics, which can contribute to their 19 resilience to SLR. Resilience here understood as the survival of the formation, even 20 if displaced in space. 21 Secondly, many researchers have already propagule 1 2 2. MATERIAL AND METHODS 3 4 2.1. Study area 5 6 Gazi Bay (4° 26’ S, 39° 30’ E) is a shallow tropical water system situated circa 7 40km south of the historic port (Kilindini) of Mombasa (Figure 1). The mangrove 8 forest covers an area of approximately 6.5 km² and is drained by two tidal creeks. 9 The tidal regime within the bay is semi-diurnal with a macro tidal range of 3.5 m 10 and an ebb dominant asymmetry (Kitheka 1996, 1997). Ten East African 11 mangrove species are present within this bay fringed with mangrove forests, 12 seagrass beds, and coral reefs, more specifically Avicennia marina (Forssk.) Vierh., 13 Bruguiera gymnorrhiza (L.) Lam., Ceriops tagal (Perr.) C. B. Robinson, Heritiera 14 littoralis Dryand., Lumnitzera racemosa Willd., Rhizophora mucronata Lam., 15 Sonneratia alba Sm., Xylocarpus granatum Koen, a second yet unidentified 16 Xylocarpus species, and Pemphis acidula Forst. (Gallin et al. 1989) (nomenclature 17 according to Tomlinson (1986)). 18 conducted throughout the western part of the bay during two dry periods (July- 19 August 2003 and 2005). 20 Mangrove species distribution within this study area was obtained by Neukermans 21 et al. (2008). 22 image was performed in combination with ground truthing based on vegetation 23 transects by the Point-Centered-Quarter Method (PCQM+) of Dahdouh-Guebas Topographical measurements (see 2.2) were A classification of a Standard QuickBird multispectral satellite 1 and Koedam (2006). The two socio-economically most important species within this 2 study area, R. mucronata and C. tagal (Dahdouh-Guebas F. et al. 2000, Dahdouh- 3 Guebas F. et al. 2004a), are mapped with User’s Accuracies above 85 percent, 4 whereas all four dominant mangrove species (A. marina (on the seaward side (Sw) 5 and the landwards side (Lw)), S. alba, R. mucronata and C. tagal (Sw and Lw)) are 6 mapped with an Overall Accuracy (OA) of 72 percent. 7 8 2.2. Topographical field survey and construction of a DTM 9 10 The aim of the topographical field surveys was to construct a digital terrain model 11 (DTM) in order to simulate water levels at present and for different 12 Intergovernmental Panel on Climate Change (IPCC) scenarios of SLR (for 13 explanation on IPCC scenarios, see 2.3). Measurements were carried out using a 14 Leica GPS-AT302 which is a centimeter-precise differential global positioning 15 system (DGPS) with a fixed reference station and a mobile rover station. Since a 16 dense mangrove cover disrupts the DGPS signal, a stratified design was applied 17 targeting the low-cover mangroves, back-mangrove areas, tidal mudflats and 18 creeks. Resolution of the DTM varies from 1m in the topographically ‘rough’ areas 19 to 50m in areas characterized by a relatively flat and even surface. All DGPS 20 points were post-processed in SKI (Static Kinematic Program) and after converting 21 these geographical coordinates into projected coordinates (WGS 1984, UTM zone 22 37S) and assigning their absolute height, a thorough knowledge of the field was 23 used to add extra points and breaklines in order to eventually optimize the 1 constructed DTM. As the height measurements of these points are relative, we 2 followed the high water line of a chosen spring tide on two consecutive days and 3 collected the X-Y-Z data of 116 points using the DGPS. Based on the Kilindini tide 4 tables (Kenya Ports Authority, KPA) the approximated absolute height of the 5 water was calculated, and the relative elevations in the DTM converted to 6 approximate absolute field topography. We recognize a temporal delay in tides 7 between Mombasa and Gazi Bay, however this does not influence our study. 8 The final coordinates resulting from the topographical measurements were 9 inserted into a geographical information system (GIS) and served as an input to 10 create a triangular irregular network (TIN) of the area. The TIN was based on the 11 (non-constrained) Delauney triangulation of the original set of points by use of 12 Voronoi diagrams, a theory for which we refer to Raper (1990). In this paper it is 13 not the intention to investigate in-depth the impact of these elevation errors 14 through Principal Component Analysis (Lopez 1997) but we give an estimation of 15 the absolute mean error and the standard deviation in densely covered and less 16 densely covered areas. After extracting 30 points respectively from each of the 17 latter areas, the TIN was reconstructed and height values were re-assessed for 18 these particular points. 19 20 21 22 23 24 1 2.3. Spatial analyses 2 3 IPCC has predicted several SLR scenarios [+ 9cm (minimum), +20cm (relative), 4 +48 cm (average) and + 88 cm (maximum)] by the year 2100 (IPCC 2001)1 based on 5 atmosphere-ocean general circulation models and emission scenarios incorporating 6 uncertainties regarding changes in terrestrial ice, permafrost and sediment 7 deposition. The main purpose of the spatial analyses is to predict possible changes 8 in vegetation assemblages under these different scenarios of SLR. 9 This modelling exercise mainly focuses on the potential of mangroves to migrate 10 towards landward areas, but it is solely based on sea level rise relative to a static 11 mangrove surface elevation. In this stage, data on sediment related changes are 12 not available, however we do not underestimate the importance of sediment in 13 mangrove vegetation dynamics in view of SLR. 14 The modelling exercise started with an assessment of the current species related 15 zonation or spatial structure present in Gazi Bay. First of all, the height 16 boundaries for each inundation class according to Watson (1928) (Table 1) was 17 defined based on the combination of the tide tables (July 2003-July 2004) 18 published by the KPA and the monthly inundation frequencies per class (Table 1). 19 In further analysis, inundation frequencies higher than those of ‘class 1’ will be 20 referred to as ‘class 0’. Using ArcGIS 8.2, these boundaries were classified into We based our analysis on SLR scenarios of the IPCC Third Assessment Report (TAR) (2001) and not on those of the Fourth Assessment Report (AR4) (2007), which respectively forecast a range from 9cm - 88cm by 2100 and a range from 18cm-59cm by 2090-2099. The reason is the following: due to lacking of published literature, AR4 models do not include uncertainties in climate-carbon cycle feedback nor do they include the full effects of changes in ice sheet flow. The AR4 projections however include a contribution due to increased ice flow from Greenland and Antarctica at the rates observed for 1993-2003, but these flow rates could increase or decrease in the future. The AR4 could have similar ranges to those of TAR if uncertainties were treated in the same way. 1 1 inundation classes based on the DTM for the current scenario versus different 2 IPCC scenarios of eustatic SLR. The relative scenario of +20 cm coincides with the 3 current trend of SLR within the long-term dataset (1985-2003) obtained from 4 gauge measurements by the Kenya Marine and Fisheries Research Institute at the 5 Kilindini Port in Mombasa. 6 Observing System’ (GLOSS) founded by the Intergovernmental Oceanographic 7 Commission (IOC) of the UNESCO. 8 Secondly, an overlay between the vegetation map and the current inundation 9 classes (Figure 2-B) gives an estimation of the vegetation surface of each species This initiative is part of the ‘Global Sea Level 10 within each inundation class. 11 classification of the vegetation, it is important to investigate whether the 12 distribution of the species within the inundation classes deviate from a random 13 distribution. To perform the statistical analyses, the complete area was divided 14 into 10 equally sized blocks. 15 calculated of each species in all inundation classes of the current situation. 16 Secondly, a Kolmogorov-Smirnov test was performed to compare the observed 17 cumulative distribution function to a theoretical normal distribution, whereafter 18 Kruskal-Wallis tests were completed to investigate if the vegetation distribution 19 within the inundation classes is random. Since the species concerned are not 20 randomly distributed, extrapolations of the vegetation structure towards future 21 IPCC scenarios of SLR were performed. The area increase (%) of each inundation 22 class within each scenario was calculated in relation to the current situation where 23 after these percentages were multiplied by the current vegetation area (ha). 24 To review the accuracy of the DTM and/or the Within each block the areal coverage (ha) was 1 2.4. Sensitivity analysis 2 3 A source of uncertainty in the input data is the DTM’s absolute height which was 4 calibrated using Kilindini port gauge measurements. To address the sensitivity of 5 the model to the absolute height uncertainty of the DTM, we investigated the 6 impact of changes in the height boundaries of the inundation classes. Upper and 7 lower height boundaries are slightly altered at a time and in a systematic manner, 8 more specifically by an increase and decrease of these boundary intervals with 5, 9 10 and 15% corresponding to 4, 6 and 8cm. The comparison between the reference 10 map (Figure 2-C1) and the output maps after altering the height boundaries was 11 assessed with an error matrix, giving Overall (OA), User’s (UA) and Producer’s 12 Accuracies (PA) (calculations see Appendix A). 13 3. RESULTS 14 15 3.1. Construction and validation of the Digital Terrain Model 16 17 The DTM of the study area is shown in Fig. 2-A. After post-processing in SKI, 4105 18 points were accepted with an average error on X, Y and Z of respectively 1.16 cm, 19 2.08 cm and 0.89 cm, whereafter several breaklines and 82 extra points were 20 manually added to optimize the DTM. 21 however crucial and had to be added as estimates (based on measurements within 22 the creek) due to high mangrove coverage. Absolute mean error and standard Breaklines along the creek banks are 1 deviation for densely covered and less densely covered areas are respectively 2 0.013m ± 0.106 and 0.089m ± 0.374. 3 4 3.2. Simulation of Sea Level Rise scenarios 5 6 The current situation covers a total (studied) area of 423.43ha, of which the 7 regularly flooded area and the non-flooded area respectively encompass 386.53ha 8 and 36.90ha. 9 scenarios (Figure 2-C1 to C5), we can conclude that there is an overall trend of 10 transgression into the terrestrial areas. Especially the maximum scenario (+88cm) 11 represents a significant area increase of ‘class 0’ and ‘class1 (AHT)’ (for 12 abbreviations see Figure 2-C). More specifically, the % area increase of these 2 13 classes from the current situation towards the maximum scenario of SLR is 14 respectively 245 and 103%. After calculating the extent of each mangrove species 15 within each current inundation class, Kolmogorov-Smirnov tests were completed 16 with results showing significance values < 0.05 for each species. The vegetation 17 distribution is therefore not normal and nonparametric techniques have to be used 18 for further analyses. 19 distribution of the vegetation within the inundation classes is not random; all 20 significance values are < 0.05. Each species evaluated within the area has a 21 preference for certain inundation classes confirming the occurrence of a specific 22 zonation or spatial structure in Gazi Bay and therefore also an adequate accuracy 23 of the field measurements. 24 When looking at the inundation classes within the different The following Kruskal-Wallis test proved that the 1 Due to the errors on the classification of the vegetation map (see 2.1 and 2 Neukermans et al. 2008)and the topographical measurements, the total area (ha) 3 occupied by each mangrove species within the whole study area (TMA) does not 4 fully coincide with the total area (ha) occupied by each mangrove species within 5 the inundation classes at present (TMAI). This however does not exceed values 6 between 2 and 12 (Table 2), except for Sonneratia alba which mainly occurs in 7 ‘class 0’ (38%) & ‘class 1 (AHT)’ (35%), consequently being the only species with a 8 high difference between TMA and TMAI of 61%. The high discrepancy between 9 TMA and TMAI for S. alba could be explained by a possible lower accuracy of the 10 DTM at the breaklines marking the creek bank. 11 All other species appear to have an adequate distribution within the whole study 12 area: Avicennia marina Sw (seaward) mainly resides in ‘class 1 (AHT)’ (26%) & 13 ‘class 2 (MHT)’ (45%), Rhizophora mucronata mainly appears in respectively ‘class 14 2 (MHT)’ (53%) and ‘class 3 (NHT)’ (22%), whilst Ceriops tagal, which is an inner 15 mangrove, occupies the areas in several mid classes. A. marina Lw (landward) 16 dominates the landward classes with 35% ‘in class 4 (SHT)’. An extrapolation of 17 changes in vegetation assemblages towards future scenarios (Figure 3-A) 18 demonstrates that, in comparison to the average scenario of SLR (+48cm), all 19 species will decrease in the maximum scenario (+88cm), resulting in a decline of 20 13% in 100 years. 21 scenario most species show a possible area increase, this is not the case for A. 22 marina Lw as this species will diminish throughout all scenarios with a highest 23 decrease of 60% in the maximum scenario. 24 economically most important species R. mucronata and C. tagal in the most Although throughout the minimum, relative and average When considering the two socio- 1 probable relative scenario of +20cm SLR, an area increase of 15% occurs in 2 comparison to the current situation. Finally, the area proportions between the 3 total mangrove area, the non-flooded area and ‘class 0’ are shown in Fig. 3-B as % 4 increase or decrease compared to the current situation. The maximum scenario 5 shows a considerable decrease in total mangrove area of 13% whereas for the 6 relative scenario this area increases with 4%. Most remarkable increase is for the 7 area ‘class 0’, namely 245% in comparison to the current situation. 8 9 10 3.3. Sensitivity analysis and error matrix for map comparison or accuracy assessment 11 12 Table B.1. (see Appendix B) shows the results of the error matrices for map 13 comparison or accuracy assessment. When comparing the vegetation distribution 14 within adjusted height boundaries for each inundation class, the outcome appears 15 to be relatively sensitive to an increase or decrease of 15%. The overall accuracy, 16 with a comparable outcome for Khat , fluctuates between 87.34 to 65.88 % when 17 considering an increase or decrease up to 10 %, yet strongly declines towards 53.61 18 to 48.02% when height boundaries of each inundation class are adjusted with 15%. 19 As the applied vegetation classification confirms the occurrence of a specific 20 zonation or spatial structure in Gazi Bay, which is highly related to inundation 21 patterns, we can conclude that sensitivity to alterations in topography can be 22 significant from a certain limit and should therefore be aligned to vegetation 23 distributions when data is available. 24 1 4. DISCUSSION 2 3 This study was to investigate whether mangrove assemblages in Gazi Bay have 4 the potential to migrate to more landward areas, which can contribute to their 5 resilience to SLR (Figure 4), understood as the survival of the formation within the 6 site. Although the focus of this study was mainly on tidal range, we emphasize the 7 importance of sediment supply, especially for scenarios of SLR higher than 8 20cm/100y (relative scenario). 9 strongly depends on the physiographic setting in which these ecosystems occur, 10 human activities that are carried out in the wetland and on how species-specific 11 competition and adaptation will unfold. There is no clear-cut answer that can be 12 applied to global mangrove coverage, yet by studying this particular mangrove 13 area with a macrotidal regime and a common vegetation zonation along a gentle 14 slope gradient from land to sea, extrapolations can be made to areas with similar 15 characteristics. 16 4.1. Vegetation dynamics of mangrove assemblages under different 17 scenarios of SLR Whether mangroves can be resilient to SLR 18 19 Bearing in mind the reductionistic approach, the extent of the most common 20 assemblages, apart from Avicennia marina and Ceriops tagal on the landward 21 side (Lw), are forecasted to increase in surface under the different scenarios of SLR 22 (except for the maximum scenario of +88cm). This forecast is in line with a few 23 earlier reports that current sea-level rise rates do not pose a threat to mangrove 1 ecosystems (e.g. McKee et al. 2007, Snedaker et al. 1994, Tan and Zhang 1997), 2 but contradicts many others (e.g. Ellison and Stoddart 1991, Fujimoto and Miyagi 3 1990, Parkinson et al. 1994, Pernetta 1993). 4 uncertainties regarding the impact of global change on mangrove growth and 5 development, such contradictions are not unexpected. 6 reductionistic approach focuses on tidal range and the possible dispersal range of 7 propagules, but it does not take into account the biogeomorphological capacity to 8 maintain or to protect a mangrove forest. 9 Landward migration of mangroves in Gazi Bay appears to be limited under the 10 maximum scenario as the highest intertidal inundation class strongly decreases 11 due to the topographical settings at the edge of the inhabited area. Consequently, 12 the landward Avicennia-dominated assemblages will continue to decrease if they 13 fail to adapt to a more frequent inundation or if competition with other species will 14 prevail. Dahdouh-Guebas et al. (2004a) made a prediction of future vegetation 15 structure in Gazi Bay based on retrospective remote sensing, social surveys and 16 tree distribution and results show that the surface extent of A. marina on the 17 landward side has been reducing since 1972. Furthermore, the current situation 18 in Gazi Bay is characterized by large bare and sandy sites on the landward side 19 which have remained in the same state for a substantial time, at least, no 20 colonization was observed for ca.16 yrs (pers. obs). When landward areas are 21 accessible during SLR, dispersal and early growth become important stages in a 22 plant life that fundamentally determine community structure and population 23 dynamics (Clarke et al. 2001, Sousa et al. 2007). 24 complex. A dense mangrove forest can provide an adequate propagule supply for However, considering the In addition, our These processes are very 1 dispersal towards newly colonisable areas, but (1) as Clarke et al. (2001) stated, 2 establishment of young trees is mainly related to the presence of parental trees 3 while this is not so much the case for juveniles and the hydrochorous dispersal of 4 propagules, and (2) suitability for stranding or self-planting of propagules is 5 strongly dependent on the presence of root structures (which can facilitate the 6 entanglement of propagules) and the compactness of the soil (clay or silt 7 dominated) (Di Nitto et al. 2008). 8 As in other transitional systems, plant establishment and community succession is 9 driven by tolerance to physiological stress and plant-plant interactions (Bertness 10 1991, Milbrandt and Tinsley 2006), hence species-specific competition could signify 11 a natural blockage for landward migration of mangroves. Yet, in several cases 12 facilitation is a common mechanism of succession in terrestrial habitats, meaning 13 that an early colonizer changes the abiotic conditions in a way that allows an entry 14 and finally a displacement of a second species to a previous intolerable habitat 15 (Connell and Slayter 1977). This was f.i. the case for (1) saltwort (Batis maritima 16 L.) as it was identified as an abundant initial colonizer of an extensive black 17 mangrove (Avicennia germinans L.) die-off area (Milbrandt & Rinsley, 2006) and 18 (2) saltmarsh cordgrass (Spartina alterniflora Loisel.) being a potential initial soil 19 stabilizer creating successional stages firstly for Laguncularia racemosa (L.) C.F. 20 Gaertn which is secondly outshaded and replaced by Avicennia schaueriana Stapf 21 & Leechm. ex Mold. (Cunha-Lignon et al. 2009). 22 The reported forecasts can also have an important socio-ecological implication. 23 Although the forest adjacent to the village has long been over-exploited for wood 24 and decreased in area, anthropogenic disturbance has diminished over the last 1 years and some mangrove assemblages have even expanded (Dahdouh-Guebas F. 2 et al. 2004a). An increase in mangrove area under different scenarios of SLR, 3 provided that it does not go at the expense of qualitative degradation, may imply 4 an increase in anthropogenic threats like f.i. traditional utilisation (McLeod and 5 Salm 2006). Clear felling of mangroves species can have severe consequences for 6 future vegetation dynamics. Furthermore, most mangrove creeks (as the case in 7 Gazi Bay) are characterized by the occurrence of time-velocity asymmetry in which 8 ebb flow is more dominant than flood flow (Kitheka 1997, 1998, Kitheka et al. 9 2002). Sediment trapping occurs during incoming flood tides and there is no 10 significant export of sediments during ebb tide (Furukawa and Wolanski 1996, 11 Wattayakorn et al. 1990), however degradation of mangroves can lower trapping 12 efficiency (Kitheka et al. 2002), consequently increasing vulnerability to sea level 13 rise. 14 15 4.2. Vegetation dynamics of individual species under different scenarios of 16 SLR 17 18 When landward areas become accessible for the migration and colonization of 19 mangrove species, we have to ask the same question as Alongi (p4, 2008): “Are 20 trends in mangrove forest replacement in response to catastrophic disturbances 21 the result of somewhat deterministic sequences as in terrestrial forests, or are they 22 the result of a stochastic, ‘first come, first served’ opportunistic response or 23 neither?”. 24 distinct succession stages, yet early sequences of species replacement are greatly Empirical data supports the idea that recovery is stochastic with 1 influenced by species present at initial recovery (Alongi 2008, Clarke et al. 2001, 2 Sousa et al. 2007). Within this study the extrapolation of the present vegetation 3 distribution towards scenarios under a rising sea level is based on species-specific 4 preference for certain inundation frequencies. The survival of these species in 5 their shift in a more landward direction is strongly dependent on their colonisation 6 rate and interspecific competition. 7 Sonneratia alba appears in vegetation zones that are daily inundated and are 8 never submitted to large salinity variations (Tomlinson 1986). When sea level 9 rises, this species is forecasted to increase in area (except under the maximum 10 scenario), yet as investigated by Dahdouh-Guebas et al. (2004a) the juvenile layer 11 within these S. alba stands is limited and propagule establishment is hampered by 12 currents that are generally known to be strongest along the seaward side (Diop et 13 al. 2001). The distribution of the young individuals of S. alba is more related to the 14 adult trees, whereas juveniles are generally spread over a wider area (Dahdouh- 15 Guebas F. et al. 2004a). The latter also applies for the species Avicennia marina 16 on the seaward side (Sw). Furthermore, Imai et al. (2006) verified that S. alba 17 seedlings and saplings, which require sunny conditions for their growth, were 18 more abundant in gaps than in the understorey. 19 landward species as Rhizophora mucronata might demonstrate that an area 20 increase of S. alba could be overestimated by our analyses. However, colonisation 21 by S. alba on seaward sand banks has occurred throughout the years. 22 Additionally, bearing in mind the site-specific rates of sea level rise and sediment 23 input rates, Ellison & Stoddart (1991) claimed that mangrove ecosystems can keep 24 pace with SLR of 8-9cm per 100 year making seaward expansion and colonisation The most seaward mangrove species Competition with a more 1 of these daily inundated areas possible. Rates of 9-12cm per 100 year cause stress 2 and adjustment to higher rates is unlikely. The minimum scenario of SLR (+9cm) 3 could in fact provide an additional and suitable habitat for S. alba and A. marina 4 (Sw). 5 R. mucronata and Ceriops tagal are two economically valuable pioneer species that 6 will most likely increase as predicted, unless anthropogenic impact rises. 7 Multivariate vegetation structure analysis showed that C. tagal is very abundant 8 in the understorey of assemblages dominated by other mangroves, which could 9 camouflage a dynamic shift (Dahdouh-Guebas F. et al. 2004a). R. mucronata and 10 C. tagal already occupy the mid zone within the mangrove area and knowledge on 11 the dispersal of their propagules indicates that prop roots and pencil roots clearly 12 have the ability to entangle propagules and that preference of propagule dispersal 13 goes to flat areas and substrates with a more compact soil structure (clay, silt) (Di 14 Nitto et al. 2008). 15 represented by a further siltation along the seaward sand bank creating a patch of 16 arid conditions and higher light intensity more favourable for A. marina 17 (Dahdouh-Guebas F. et al. 2004a). 18 Avicennia marina (Lw) will have to adapt to longer inundation frequencies. It is 19 known that this species can tolerate high salinity variation, so could the double 20 zonation of this species on the landward side versus the same species on the 21 seaward side support the idea of dynamic adaptation? Genetic analyses based on 22 48 RAPD (Randomly Amplified Polymorphic DNA) loci have demonstrated that 4 23 DNA fragments show a slight differentiation in allelic frequency between the two 24 A. marina stands in spite of their short distance separation (Dahdouh-Guebas F. et One disadvantage for R. mucronata could however be 1 al. 2004b). This indicates that there is less genetic exchange between the 2 disjunctive stands than within one stand, consequently suggesting that an 3 ecological or physical barrier might exists. 4 dispersal of propagules in both directions however obstruction by complex root 5 structures can prevent this exchange. Additionally, interspecific competition with 6 the adjacent species C. tagal could disadvantage A. marina as McCusker (1977) 7 confirms that a salinity increase causes a reduction in water use efficiency for the 8 seedlings of Rhizophora, but not for Ceriops. Furthermore, an elevated CO2 level 9 will enhance the efficiency of water use (UNEP 1994), however this advantage is 10 lost when salinity becomes too high for instance at low inundation frequency areas 11 at the landward side. 12 temperature, since this species has lowest optimal temperature for leaf 13 development (Hutchings and Saenger 1987). 14 There are several well-established physiologic mechanisms influencing mangrove 15 community composition (Duke et al. 1998, McKee 1995), yet research is needed on 16 interspecies interactions influencing mangrove forest regeneration in post- 17 disturbance mangrove communities. Tidal range might facilitate the Another drawback for A. marina is an increase of 18 19 4.3. Recommendation for further research and management strategies 20 21 In the light of mangrove ecosystem stresses caused by climate change, managers 22 face the dual challenge of selecting and implementing conservation strategies in 23 order to maintain and restore resilient mangrove forests. 1 In this study the emphasis resides on tidal range and not on sediment supply, 2 however, we give a preliminary vulnerability assessment of this mangrove area 3 based on a slightly adjusted decision tree (Figure 5) to aid resilient site selection 4 for mangroves by McLeod & Salm (2006). This decision tree was applied after 5 appointing Gazi Bay as a high biodiversity candidate site based on biological and 6 environmental criteria (Table C.1, see Appendix C). Decisions were made based on 7 available literature involving the mangrove area in Gazi Bay and the relative SLR 8 scenario of +20 cm, which coincides with the current trend along the Kenyan 9 Coast. 10 Following this decision tree, the mangrove area in Gazi Bay appears to be 11 adequately resilient for at least 100 years and can most likely be appointed as a 12 Marine Protected Area (MPA). However we do not intend to focus only on MPA’s, 13 yet we want to anticipate to a future scenario of sea level rise and indicate gaps in 14 on the one hand scientific and on the other hand site-specific knowledge that 15 necessitates further research. Given (1) the macrotidal regime and permanent 16 rivers and creeks that provide freshwater and sediment (mainly during wet 17 season), (2) the knowledge that the drainage basin of both Mkurumuji and 18 Kidogoweni rivers, which extend into the coastal ranges of the Nature Reserve 19 ‘Shimba Hills’, has limited anthropogenic pressures with respect to the intactness 20 of the hydrological regime, and (3) that landward migration in Gazi Bay is possible 21 under the relative scenario of sea level rise, the decision tree leads us towards the 22 question whether recruitment is strong. The answer is definitely ‘yes’, however we 23 feel that the possibility of a shift in vegetation structure needs to be implemented, 1 rendering Gazi Bay into a site that is ‘Maybe OK for MPA’. According to McLeod & 2 Salm (2006) the decision tree would have led towards ‘Good choice for MPA’. 3 4 The recommendations for further research and management strategies, which can 5 be applied globally, are the following: 1) identifying an early colonizer to promote 6 early establishment of mangrove seedlings, 2) measuring changes in elevation by 7 means of Surface Elevation Tables (SETs), 3) Assuring the possibility of landward 8 migration and (4) investigating propagule dispersal by combined hydrodynamic 9 and ecological behaviour modeling. 10 11 1 Appendix A: Calculation of an error matrix for map comparison or 2 accuracy assessment 3 4 Map 1= raster grid of n classes as a model output 5 6 Map 2= raster grid of n classes from an alternative model or comparison reference 7 layer. 8 9 Producer’s accuracy (PA) 10 11 Takes into account the accuracy of individual classes and therefore indicates the 12 probability of the cell value in Map 2 being the same as in Map 1. 13 14 = xii / x+i * 100% 15 16 xii= total number of correct cells in a class 17 x+i= sum of cell values in the column 18 19 User’s accuracy (UA) 20 21 Takes into account the accuracy of individual classes but indicates the probability 22 of the cell value in Map 1 being the same as in Map 2. 23 1 = xii / xi+ * 100% 2 3 xii= total number of correct cells in a class 4 xi+= sum of cell values in the row 5 6 Overall Accuracy (OA) 7 8 Summarizes the total agreement / disagreement between the maps and 9 incorporates the major diagonal while excluding the omission and the commission 10 errors. 11 12 = D/ N * 100% 13 D= total number correct cells as summed along the major diagonal 14 N= total number of cells in the error matrix 15 16 Khat 17 18 Measure of agreement or accuracy based on KAPPA analysis to compare maps of 19 similar categories in order to determine if they are significantly different 20 21 =N ((Σri=1 xii - Σri=1 (xi+ * x+i)) / (N² - Σri=1 (xi+ * x+i))) 22 23 r= number of rows in the matrix 24 xii= total number correct cells in a class (i.e. value in row i and column i) 1 xi+= total for row i 2 x+i= total for column i 3 N= total number of cells in the error matrix 4 Appendix B Input parameter Adjustments in Comparison input criteria of vegetation distributions within the adjusted inundation classes Overall accuracy K hat (%) (%) Inundation +5% 87.34 85.34 classes +10% 76.67 75.3 1 (height +15% 48.02 49.61 boundaries) -5% 78.09 75.28 -10% 65.88 65.39 -15% 53.61 50.72 5 6 Table B.1: Results of the error matrices for map comparison or accuracy assessment when 7 comparing the vegetation distribution within adjusted height boundaries for the inundation 8 classes. Values represent the overall accuracy and Khat in percentages. 9 10 11 12 13 14 1 Appendix C: Mangrove resilience factors: Case study: Gazi Bay, Kenya 2 Factors that allow for peat building to keep up with sea-level rise Association with drainage systems including permanent rivers and Applicable to Gazi Bay Literature available Yes / No per factor Yes creeks that provide (e.g. Dahdouh- freshwater and sediment Guebas F. et al. 2004a, Kitheka Sediment rich-macrotidal environments to facilitate sediment Yes 1996, 1997, Njambuya 2006, redistribution and accretion Obade et al. 2004, Actively prograding coast and delta Yes Ohowa et al. 1997) Natural features (bays, barrier islands, beaches, sandbars, reefs) that reduce wave erosion and storm surge Yes Factors that allow for landward migration (e.g. Di Nitto et al. Mangroves backed by low-lying retreat areas (for example, salt flats, marshes, coastal No/Yes 2008, Neukermans in certain places et al. 2008, Obade et plains) which may provide suitable habitat for colonization and al. 2004) landward movement of mangroves as sea level rises Mangroves in remote areas and distant from human settlements and agriculture, aquaculture, and salt production developments Yes Mangroves in areas where abandoned alternate land use provides opportunities for Yes, unmanaged coconut plantations restoration, for example, flooded villages, tsunami-prone land, unproductive ponds Factors that enhance sediment distribution and propagule dispersal Unencumbered tidal creeks and areas with a large tidal range to Yes (e.g. De Ryck 2009, improve flushing, reduce ponding and stagnation, and enhance Di Nitto et al. 2008, sediment distribution and propagule dispersal Kitheka 1996, 1997, Ohowa et al. 1997) Areas with a large tidal range may be better able to adjust to increases in sea level due to stress tolerance Yes Permanent strong currents to redistribute sediment and maintain Yes open channels Factors that indicate survival over time (e.g. Beeckman et Diverse species assemblage and clear zonation over range of Yes elevation (intertidal to dry land) al. 1989, Bosire J. et al. 2008a, Bosire J. O. et al. 2006, Range in size from new recruits to maximum size class (location Yes Dahdouh-Guebas F. et al. 2002a, and species dependent) Dahdouh-Guebas F. Tidal creek and channel banks consolidated by continuous dense Yes mangrove forest (which will keep these channels open) et al. 2004a, Dahdouh-Guebas F. et al. 2002b, Kairo Healthy mangrove systems in areas which have been exposed to No J.G. 2001, Kairo J. large increases in sea level due to climate induced sea-level rise G. et al. 2001, and tectonic subsidence Neukermans et al. 2008, Tack et al. 1992, Van Tendeloo 2004) Factors that indicate strong recovery potential Access to healthy supply of propagules, either internally or from Yes adjacent mangrove areas Strong mangrove recruitment indicated by the presence, variety, Yes and abundance of established mangrove propagules Close proximity and connectivity to neighbouring stands of healthy Yes mangroves Access to sediment and freshwater Yes Limited anthropogenic stress Yes, no major residential area in the vicinity, selected as a fairly pristine East African site in the EU PUMPSEA project: http://www.pumpsea.icat.fc.ul.pt Unimpeded or easily restorable hydrological regime Yes Effective management regime in place such as the control of usual Yes threats like dredging and filling, conversion to aquaculture ponds, construction of dams, roads, and dikes that disrupt hydrological regime etc. Integrated Coastal Management Plan or Protected Area Yes/No Management Plan implemented 1 Table C.1: Mangrove resilience factors that inform site selection (according to McLeod & Salm, 2006) Case study: Gazi Bay, Kenya 1 2 5. ACKNOWLEDGEMENTS 3 4 Many thanks are due to the people of Gazi Bay, more specifically Latifa S. 5 Ba’alawy and her relatives for the hospitable family environment and R. Abdul for 6 the assistance on the field. This research was funded the Flemish Interuniversity 7 Council (VLIR) and the Fonds David & Alice Van Buuren. D.D. has a VLIR PhD 8 Scholarship. This work was in part presented at (1) the International Symposium 9 of Aquatic Vascular Plants (ISAVP) (January 11–13, 2006, Brussels, Belgium) (2) 10 the 7th International Symposium on GIS and Computer Cartography for Coastal 11 Zone Management (CoastGIS) (July 12–16, 2006, Wollongong, Australia) and (3) 12 MMM3 Meeting on Mangrove ecology, functioning and Management (2-6 July 13 2012, Galle, Sri Lanka). 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 6. REFERENCES Abuodha PAW, Kairo JG. 2001. Human-induced stresses on mangrove swamps along the Kenyan coast. Hydrobiologia 458: 255-265. Allen JA, Krauss KW. 2006. 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Inundation Flooded by classes Monthly Present situation Minimum Relative scenario, Average scenario, Maximum scenario, inundation (m) scenario, +9cm +20cm +48cm +88cm (m) (m) (m) (m) frequency 1 All high tides 56-62 2.10-2.60 2.19-2.69 2.30-2.80 2.58-3.08 2.98-3.48 45-56 2.60-3.10 2.69-3.19 2.80-3.30 3.08-3.58 3.48-3.98 20-45 3.10-3.50 3.19-3.59 3.30-3.70 3.58-3.98 3.98-4.38 2-20 3.50-3.80 3.59-3.89 3.70-4.10 3.98-4.28 4.38-4.68 0-2 3.80-4.20 3.89-4.29 4.10-4.40 4.28-4.68 4.68-5.08 (AHT) 2 Medium high tides(MHT) 3 Normal high tides (NHT) 4 Spring high tides (SHT) 5 Abnormal (equinoctial tides) (EHT) 1 Table 2: Presentation of the total area (ha) occupied by each mangrove species with the 2 whole studied area (TMA) and the total area occupied by each mangrove species within the 3 inundation classes at present (TMAI). Sw= seaward side and Lw= landward side. 4 Species TMA TMAI Difference 5% 6 7 Avicennia marina Sw 46.59 41.65 11.86 8 9 Avicennia marina Lw 32.34 29.53 9.52 10 11 12 Ceriops tagal Lw 37.99 36.53 3.99 13 14 Ceriops tagal Sw 13.50 13.27 1.76 15 16 17 Rhizophora mucronata 109.42 105.50 3.71 18 19 Sonneratia alba 7.96 4.95 60.89 20 21 22 23 1 2 Figure 1: Representation of (A) the Kenyan coast (Dahdouh-Guebas F. et al. 2000) and (B) 3 Gazi Bay. 4 research focuses on the western part as encompassed by the overlaid vegetation map. S. 5 alba = Sonneratia alba, R. mucronata= Rhizophora mucronata, C. tagal Lw= Ceriops tagal 6 on the landward side, C. tagal Sw= Ceriops tagal on the seaward side A. marina Lw= 7 Avicennia marina on the landward side and A. marina Sw= Avicennia marina on the 8 landward side. Classification of the mangrove species coverage was obtained by 9 Neukermans et al. (2008). The satellite image (Quickbird) shows the whole bay of Gazi ; however this 10 11 Figure 2: (A) Presentation of the DTM, (B) 3D presentation of the combination between 12 (B1) inundation classes, (B2) vegetation map and (B3) Quickbird image, (C) Presentation of 13 the inundation classes: (C1) Current situation, (C2) Scenario +9cm, (C3) Scenario +20cm, 14 (C4) Scenario +48cm, (C5) Scenario +88 cm. AHT= all high tides, MHT= medium high 15 tides, NHT= normal high tides, SHT= spring high tides and EHT= equinoctial high tides. 16 Inundation frequencies higher than those of ‘class 1’ will be referred to as ‘class 0’. 17 18 Figure 3: (A) Graph of the total area (ha) per species within the 4 SLR scenarios, (B) Future 19 prediction of the dynamics of mangroves, non-flooded area and ‘class 0’. 20 21 Figure 4: Overview scheme summarizing the discussion on resilience of mangroves facing 22 sea level rise, more specifically concerning the case study in Gazi Bay (Kenya). 23 24 Figure 5: 25 26 Figure 6: Decision tree to aid resilient site selection for mangroves according to McLeod & 27 Salm (2006). The latter can be applied once candidate sites of high biodiversity have been 28 selected using biological criteria f.i. *factors that indicate strong recovery potential (see 1 Appendix, Table A.2). This decision tree was adjusted (**) to implement the possibility of a 2 shift in vegetation structure. MPA= Marine Protected Area. 3 1 Figure 1 1 2 Figure 3 Figure 2 1 2 3 4 5 6 7 Figure 4 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 Figure 5 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Figure 6 1