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PGR9 – Confirmation of Candidature V1.0 Update April 26 of 35

F O R M P G R 9 C O N F I R M A T I O N O F C A N D I D A T U R E R E S E A R C H P R O P O S A L ( F O R M E R D 9 )

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Working Title Variation of mangrove functional traits underlying organic matter production in tree biomass and sediments: a comparative study of temperate and semi-arid tropical mangroves under different anthropogenic pressures

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Mangrove forests appear to be one of the most productive ecosystems in the world. These forests can store an average of 1,023 Mg of carbon per hectare. Most of this carbon enters the ecosystem via mangrove trees net primary production during photosynthesis. The above ground biomass may store up to 170 Mg of this total carbon stock, while 850 Mg is accumulated in the soil through wood, roots and litter burial. However, there are significant differences in the literature regarding the production and retention of organic matter among sites. These differences are apparent among mangrove stands under different climates, with net primary production decreasing significantly from the equator to the tropics. Local scale variations can be observed depending on a range of factors, such as anthropogenic pressure. The present research project intends to investigate how climatic (latitudinal difference) and anthropogenic (i.e., sedimentation, aquaculture and mining) pressures influence tree functional traits, which may drive production of organic matter in mangrove (Avicennia marina subsp. australasica) tree biomass and sediments. The project will focus on comparing these ecological processes between temperate (New Zealand, 36°S) and semi-arid tropical (New Caledonia, 21°S) mangroves. Specifically, the project aims to (i) characterize variations in functional traits driving mangrove growth rates (relative growth rates, stomatal conductance, Specific Leaf Area (SLA), chlorophyll content, nutrient content) in response to cold and drought stress, under different anthropogenic pressures, and (ii) understand how these traits influence the quality of the organic matter (nutrients and lignin content) accumulated on the forest floor and their degradation rates.


R E S E A R C H P R O P O S A L Rationale and Significance of the Study This research is of international significance to the field of mangrove ecosystem functioning and productivity, since it will be the first comprehensive comparative study of its kind. The comparisons between

semi-arid tropical and temperate mangroves will likely contribute to our understanding of the impacts of global change on mangrove habitats and adjacent coastal marine ecosystems.

Furthermore, at a local scale, the research will be directly relevant to sustainable management and conservation of these coastal ecosystems.

In New Zealand for instance, mangroves have expanded over the last decades, and concerns arise now on whether they should be removed or on the contrary preserved at all cost. Such management decisions are

hampered on the one hand by the lack of information on temperate mangrove ecological functioning, and on the other hand by the important differences in structure and productivity observed between mangrove stands locally. Gaps remain in the understanding of the mechanisms leading to these variations and as to

what extent these stands differ in ecological value (e.g., biodiversity, water quality, erosion control). The present research aims to study the mechanisms of such variations under different nutrient regimes. The

findings of this work will be directly relevant to local authorities and decision makers seeking to conserve and manage these ecosystems.

In New Caledonia, the study of mangrove production is also of prime interest. Mangroves in New Caledonia are found at the edge of one of the richest coral reefs in the world and support a large biodiversity through

their role as nurseries and erosion control. The present study will evaluate the impact of two important economic activities of the island (mining and aquaculture) on mangrove net primary production, and organic

matter decomposition. A better understanding of the impact of transition metals and nutrient input on mangrove species is critical to evaluate the resilience of mangroves to these anthropogenic pressures.

Literature/Past Research Review

1. Introduction: Mangrove Ecological Services and Productivity

Mangrove habitats are forested plant communities, found in sheltered coastlines, estuaries and deltas of tropical and subtropical regions (Saenger 2002, Duke 2006a, FAO 2007). Worldwide, mangroves occupy about 138,000

km², across 118 countries and territories (Giri et al. 2011). These intertidal forests also border the largest human populations in the world, whose livelihoods depend mostly on the fisheries resources they provide together with

intertidal flats, coral reefs and seagrass ecosystems (Rönnbäck 1999, Barbier 2000). Furthermore, mangrove ecosystems protect shorelines from waves and wind and maintain the structure of the coast by preventing erosion (Danielsen et al. 2005, Dahdouh-Guebas et al. 2005, Gedan et al. 2011). These forests also constitute

traps for sediment and act as a filter for detritus and toxic elements, thus improving the quality of coastal waters (Harbison 1986, Lacerda 1998, Furukawa et al. 1997, Marchand et al. 2011a, b, 2012). Mangroves play an

important role in the cycle of nutrients, and contribute directly and indirectly to a high secondary productivity in tropical coastal ecosystems (Alongi 1996, Mumby et al. 2004, Nagelkerken et al. 2008). Finally, the interest

generated by the goods and services provided by mangroves has recently been enhanced by the demonstration of their significant capacity to store carbon. Mangroves appear to be one of the most productive ecosystems in

the world. Furthermore, they can store an average of 1,023 Mg of carbon per hectare in soil sediments and standing biomass (Twilley et al. 1992, Kristensen et al. 2008, Donato et al. 2011). Due to their high capacity of


production and storage, and the numerous services they provide, conservation and development of these

ecosystems are therefore of prime interest in the context of carbon sequestration (Mcleod et al. 2011, Siikamäki et al. 2012). These ecosystems are projected to experience severe modification of their diversity, functioning and

geographic distribution in the face of global changes and added anthropogenic pressures (Ellison 1993, 2005, Field 1995, Sheves et al. 2007, Lovelock and Ellison 2007). As a consequence, it has become increasingly

important to understand the mechanisms underlying differences of net primary production (NPP) and carbon storage under different climates and under different anthropogenic pressures.

2. Carbon stocks and fate of mangrove primary productivity

In light of their high capacity of production and

the importance of mangroves for associated ecosystems, the last decades have seen an

increasing interest from the scientific community to the evaluation of carbon stocks and their dynamics within mangrove forests

(Alongi et al. 2003, 2014, Bouillon et al. 2008, Dittmar et al. 2006, Adame & Lovelock 2011,

Léopold et al. 2012, 2013, Kauffman and Donato 2012, Sukardjo et al. 2013). According to the last

estimates (Alongi 2014), mangrove forests worldwide present a total gross primary

production (GPP) of 699 Tg C y-1, 91% of which is fixed from the atmosphere by mangrove trees

through photosynthesis, and the remaining 9% is fixed by micro- and macro-algae (Figure 1).

Respiration accounts for 65% of the GPP, and the remaining 210 Tg C y-1 are invested in plant biomass (NPP).

Approximately 10 Tg C y-1 of litter and 5 Tg C y-1 of dead roots are then buried in the soil (Alongi et al. 2003, 2004), along with 9 Tg C y-1 imported from upstream and adjacent ecosystems. The remaining litter is exported to

adjacent ecosystems as particulate organic carbon (POC, Dittmar et al. 2006, Adame and Lovelock 2011) and dissolved inorganic carbon (DIC). This export of carbon towards adjacent ecosystems accounts for 10 to 11% of

the total terrestrial organic carbon input in the oceans. From this global budget, it follows that almost half of the total mangrove litterfall (15 Tg C y-1) remains in the ecosystem. This litterfall is either buried and/or consumed by

crabs and/or snails (Robertson et al. 1989, Koch and Wolff 2002), and/or enter in the first levels of the benthic detritus food chain via fungi, protozoa, bacteria, which recycle and reallocate this important pool of nutrients

within the ecosystem.

3. Ecophysiological Determinants of Mangrove Tree Net Primary Production

Differences in mangrove productivity along the latitudinal gradient:

Figure 1. Fate of mangrove global primary production (Tg C y-1) based on the estimate of 138,000 km² of Giri et al. (2010, Alongi 2014).


If mangroves are undeniably

highly productive ecosystems, significant differences of

functionality and productivity emerge in the literature

at different spatial scales (Saenger and Snedaker 1993, Lovelock et al.

2007, Bouillon et al. 2008). The main trend in NPP is observed along the latitudinal gradient, in mangrove

stands under different latitudes and therefore under different climates

globally. Recent studies show that, as with terrestrial ecosystems, mangrove

net day time canopy production shows an important decline from the equator to the tropics (Alongi 2009, 2014), with a decrease in tree height

(Kuchler 1972, Wells 1983, Woodroffe and Grindrod 1991) and lower litterfall values and above-ground biomass in temperate mangrove forests (Saenger and Snedaker 1993, Alongi 2009, 2014, Figure 2).

A commonly accepted hypothesis to explain this decrease in productivity with increasing latitude is a growth

rate limitation induced by a briefer optimal season. At higher latitudes, drought or winter temperatures limit the geographic distribution of most mangrove species, and only some drought or cold resistant species persist.

For the most cold-resistant mangrove species, such as Avicennia marina, low temperatures decrease stomatal conductance and the electron transfer rates, possibly by a simultaneous decrease in total chlorophyll concentration and synthesis of protein D1 within the Photosystem II (Kao et al. 2004). As a consequence,

temperate mangroves show lower photosynthetic CO2 assimilation rates and lower global performances than those in the tropics. This seasonal effect is exacerbated by the lower amounts of solar radiation and shorter

day lengths away from the equator. Thus, periods of lower light intensity induce lower growth rates (Smith 1987) due to lower photosynthesis capacity of the photosystem I and II (Kao et al. 2004, Lambers et al. 2008).

Finally, other mechanisms involved in cold-resistance, such as production of osmoregulatory metabolites to avoid chilling injury (Sakamoto and Murata 2000), are energy consuming and could contribute to reduce the

amount of resources allocated to biomass production.

The same decreasing performance response can be observed in the case of drought stress, under semi-arid and arid climates. At the physiological level, the biggest challenge for species living in these flooded, anoxic

and salty environments is indeed to minimize their water losses, while maximizing carbon gains to meet all their metabolic and reproductive functions. Mangrove plants are highly successful in these environments due

to their high water use efficiency and their low stomatal conductance under these environmental stresses (Andrews and Muller 1984). Indeed, hypoxia or anoxia generated by recurrent flooding inhibits O2 diffusion from the atmosphere to the plant tissues and necessitates special adaptations. For mangrove species, O2

diffusion while in waterlogged soils takes place through a system of tissues filled with air, such as aerenchyma and lenticels on the bark and aerial roots (prop, plank and knee roots or pneumatophores) (Tomlinson 1986).

During high tide, the filling of water within these intercellular spaces is then prevented by closing the stomata

Figure 2. Regression of mangroves above-ground biomass (dry weight/ha) according the latitude (Alongi 2009, using data of Saenger & Snedaker 1993, Clough 1998, Fromard et al. 1998, Alongi & Dixon 2000, Alongi et al. 2000a, b, 2004a, 2005b, Sherman et al. 2003, Alongi & de Carvalho 2008).


apertures in order to maintain a low transpiration rate within the leaves, thus maintaining a high water

potentials to prevent water uptake. The counterpart of these adaptations is a greater investment in root structures for some species (Ye et al. 2003), lower nutrient and water uptakes, and lower CO2 assimilation,

photosynthesis and growth rates for individuals growing seaward compared to landward (Ball 1988). In a similar manner to flooding, drought stress induces the closure of the stomatal aperture to avoid water loss by

transpiration, and contributes therefore to enhance this water stress. These limited gas assimilation, and water deficit lead to lower production in semi-arid tropical areas than in the tropics.

Conversely, mangrove stands at high latitudes have to compensate their shorter growing periods by having higher performances during favourable seasons, to grow and reproduce in a shorter period of time compared

to their tropical counterparts (Sterner and Elser 2002, Kerkhoff et al. 2005). This trend has been observed in terrestrial plants and in mangrove stands in situ between 36°S and 27°N (Lovelock et al. 2007). For terrestrial

plants, this adaptation is demonstrated at high latitudes and low temperature by plants having higher nutrient contents in leaves (especially nitrogen and phosphorous, Reich and Oleksyn 2004), higher nutrient use

efficiencies (Körner and Diemer 1987, Kerkhoff et al. 2005) and higher resorption efficiencies (Nordell and Karlsson 1995). These processes reflect higher nutrients requirements during the short favourable period of

growth. A latitudinal trend in nutrient stoichiometry within plant tissues has also been observed for terrestrial plants, with a decrease in N:P ratios in litter with increasing latitudes and decreasing temperature. This ratio

decreases either because higher growth rates require higher amounts of phosphorous than nitrogen for metabolic purposes (“growth rate hypothesis”, Elser et al. 2000, Kerkhoff et al. 2005), or because tropical soils

(older and more weathered) contain lower amounts of phosphorous than those of temperate soils (“geochemical hypothesis”), which is indicated by N:P ratios being driven by nutrient limitations (Vitousek

1984, Vitousek and Sandford 1986, Reich and Oleksyn 2004). In a recent study, Lovelock et al. (2007) addressed the question of nutrient traits in mangrove stands (Rhizophora and Avicennia) which were growing at different latitudes through a fertilization experiment, and showed similar latitudinal trends in mangrove

forests. That is, mangrove growth rates, N and P concentrations in tissues (adjusted for temperature) also increased with latitude for both genera studied. However, nutrient use efficiency (i.e., the amount of nutrients

by unit of weight produced), particularly photosynthetic P use efficiency and P resorption efficiency before senescence, decreased with increasing latitude. This last trend differs from that observed in terrestrial taxa

and reflects perhaps higher limitation of P in tropical mangroves ecosystems.

4. Fate of mangrove organic matter production in sediments Mangrove NPP is also a function of the capacity for organic matter and nutrients to be decomposed and

recycled in the ecosystem, and available again for plants nutrition (Feller 1995, McKee et al. 2002). Litter from plants and algae are the most important autochthonous sources of organic matter and nutrient contents in

the sediments (Alongi 1998, Kristensen et al. 2008). Their nutrient status and their decomposition rates vary according to numerous factors. For example, one factor can be the age of the stand observed. For instance,

mature forests have higher total organic carbon, carbon nitrogen ratios and higher organic matter contents than forests at pioneer stages, and have lower decomposition rates than young forests (Marchand 2003, Li

and Ye 2014). This pattern is due to an accumulation over time of dead fine roots, litter, wood, and of molecules recalcitrant to degradation, such as lignin compounds. Organic matter turnover depends also on

the geochemical conditions of the sediments (Marchand et al. 2005), their temperature, texture, rate of


inundation, pH and redox potential among others, which drive the composition of animal and aerobic and

anaerobic microbial communities responsible for plant material fragmentation and decomposition. An important factor of plant material degradation remains in its initial composition, and thus the nutrient

strategies of the species it belongs to. Inter and intra specific variations of the litter composition have been observed in mangrove ecosystems (Tam et al. 1998). For example, in Avicennia spp., leaves have lower tannin

contents and higher concentrations of nitrogen, thus lower C/N ratios, than species from the Rhizophoraceae family. These low C/N ratios are highly preferred by the community of organisms involved in litterfall

decomposition pathways, and are as such decomposed faster (Kristensen et al. 2008). Intraspecific variations in litter composition are mostly due to a difference in the pool of nutrients between locations, and can therefore vary according to allochthonous inputs, anthropogenic pressures, or between different climates, as

explained previously. The status of nutrient limitation drives the degree of nutrient conservation and use efficiency in mangroves. Compared to terrestrial plants, mangroves have been found to have a stronger

capacity of nutrient conservation, partly due to an active resorption process during senescence (Wong et al. 2003, Lin et al. 2007, 2010). In the case of low nutrient availability, mangroves can withdraw nutrients before

leaves abscission, reducing the amount of nutrients in the litterfall (Reef et al. 2010). From the findings presented above, it appears that differences of NPP between latitudes and anthropogenic pressures depend on a large number of factors, often correlated, and that their impacts tend to be site-

specific. They depend on the one hand on the synergetic impacts of environmental, climatic and anthropogenic parameters encountered in the area of study, and on the other hand on the species

composition and their physiological adaptations to the environment. However, few studies have investigated the physiological adaptations to environmental constrains at different spatial scales in situ in temperate and

semi-arid climates, and how these adaptations influence mangrove forest production and plant-soil feedback.

Aims and objectives of the study: The present research intends to investigate the effects of geochemical and climatic conditions on mangrove physiology and growth, and plant-sediment nutrient relationship within mangrove stands exposed to different

anthropogenic pressures in temperate New Zealand and semi-arid New Caledonia.

Aim 1: Investigation of the relationship between mangrove functional traits and growth rates:

• Objective 1: To analyze, model and compare mangrove growth rates within different locations (different estuaries) and geographic regions (New Zealand and New Caledonia) over two seasons

(winter and summer).

• Objective 2: To analyze, model and compare mangrove functional traits (stomatal conductance, SLA,

chlorophyll and nutrient contents, and nutrient use efficiency) within different locations and geographic regions over two seasons.

• Objective 3: To identify the correlation between mangrove functional traits and growth rates within different locations and geographic regions over two seasons.

Aim 2: Identification of the effect of geochemical and climatic parameters on mangrove functional traits and growth rates:


• Objective 1: To characterize environmental variables (temperature, salinity, pH, redox potential, rain

fall, nutrients) within different locations and geographic regions over two seasons.

• Objective 2: To determine the relative and synergistic contribution of different geochemical (salinity,

pH, redox potential, nutrients) and climatic (temperature, rain fall) parameters to mangrove functional traits and growth rates.

Aim 3: Identification of the effect of mangrove functional traits on litter quality:

• Objective 1: To characterize the litter quality (total organic carbon, nutrient contents, nutrient

resorption efficiency, lignin content) prior decomposition within different locations (different

estuaries) and geographic regions (New Zealand and New Caledonia) over two seasons (winter and

summer).

• Objective 2: To identify the relationship between functional traits and litter quality within mangrove

stands exposed to different anthropogenic pressures and geographic regions over two seasons.

Aim 4: Comparison of litter degradation rates under different latitudes and anthropogenic pressures

• Objective 1: To identify the decomposition rate of litter fall within mangrove stands at different

locations (different estuaries) and geographic regions (New Zealand and New Caledonia) over two

seasons (winter and summer).

• Objective 2: To determine the contribution of mangrove organic matter (via lignin-derived phenol

ratios) in the sediment within mangrove stands at different locations and geographic regions over two

seasons.

Study Design

Several mangrove stands will be investigated at two different latitudes: 1) a temperate maritime climate (New

Zealand) and 2) a semi-arid tropical climate (New Caledonia). Within each geographic region, sites will be

selected with different anthropogenic characteristics (i.e. aquaculture, mining in New Caledonia, and

sedimentation due to land-use changes in New Zealand).

One temperate mangrove stand (in Mangawhai Harbour) will be located in the North Island, New Zealand

(36°S). These locations will be exposed to similar climatic conditions. Winter is characterized by high

precipitation (ranging from 100 to 120 mm from May to August) and low temperatures (8 to 15°C), while the

summer is warmer (15 to 25°C from December to March) and presents lower precipitation (70 to 90 mm).

Only one mangrove species, Avicennia marina (Forsk.) Vierh subsp. australasica, (Walp.) J. Everett, is found in

New Zealand. This species is distributed in the North Island, between the latitudes 36°S and 38°S and is able

to resist chilling temperature and occasional frosts as cold as -3°C (Sakai and Wardle 1978). In general, these

monospecific stands are characterized by short trees, ranging from 3-4 m tall stunted shrubs at the southern

end of mangrove distribution, to 8-10 m tall at the northern end (Woodroffe and Grindrod 1991). Important

size variations can be observed within a same estuary depending on proximity to tidal creeks (Woodroffe


1985). Unlike the general trend observed worldwide, mangroves in New Zealand have extended these last

decades, from ~19,000 ha in 1986 to 22,000 ha in 1997 and 26,000 ha in 2007 (FAO 2005, Morissey et al.

2007). This expansion is likely to be due to sediment deposits along the shores following land-use changes

(deforestation, development of agriculture and urban areas). These sediment build-ups create new habitats

for mangrove seawards, and facilitate mangrove colonization by their enrichment in nutrients, particularly

nitrogen (Lovelock et al. 2007b).

Three contrasting study sites will be selected in New Zealand along the estuary of Mangawhai Harbour (36°

07’ S, 174° 36’ E). Mangawhai Estuary (248 ha) is located 100 km north of Auckland City and comprises a

mangrove area of 87 ha. Three sampling sites will be selected within this estuary with, from north to south,

Jack Boyd, Insley and Molesworth. These sites differ in the textural properties of the sediments, their nutrient

availability and the size of the trees: Jack Boyd has tall and dense trees and muddy sediment; Insley Street has

short sparse shrubs with sandy sediment and Molesworth has dense to sparse medium trees, with muddy to

sandy sediment. The characteristics of these sites are listed in Table 1 and Figure 3 (a, b).

Semi-arid tropical mangrove stands will be selected on the west coast of New Caledonia (21° S). Summer

occurs from December to March (26 to 28°C), and is characterized by high average monthly precipitation (80

to 140 mm), while the dry season presents colder temperature (16 to 25°C) and lower precipitation (40 to 70

mm). A period of transition with high precipitation is observed in May and June (90 and 130mm respectively).

Mangroves in New Caledonia cover approximately 25,000 hectares (79 % of which is located on the west coast

and 19% on the east coast) and are associated with 24,000 km² of coral reefs, including the second largest

barrier reef in the world (Zann and Vuki 2000, Ellison 2009). Mangroves in New Caledonia are the third richest

in terms of floristic diversity in the Pacific, with 24 species of mangroves (Duke 2006b, Virly 2006), including

three hybrids of Rhizophora (Ellison 2009). This genus alone accounts for 50% of New Caledonia’s mangroves,

while the only Avicennia species, A. marina subsp. australasica, covers 15%. Some mangrove stands are

relatively unaffected by human activities, while others are strongly affected by discharges of effluents from

the 700 ha of aquaculture ponds, one of the first economic activities of the Island (Molnar et al. 2013). Another

pressure on mangroves is mining activities. Many mangrove stands are exposed to water inputs rich in trace

metals, particularly Ni, Fe and Cr, derived from mining exploitation and natural erosion of lateritic soils

upstream, which cover a large part of the Island (Marchand et al. 2011a, 2012).

Three sites differing in degree of anthropogenic pressures will be studied along the west coast of the North

Province. From north to south, the mangrove stand of “Coeur de Voh”, a mangrove stand in Vavouto Bay, and

the mangrove of the FAO (“Aquaculture Farm of the Ouenghi”), in the Ouenghi Harbour.

The site of Coeur de Voh (20°56' S, 164°39' E) is a heart-shaped mangrove of four ha, undisturbed by direct

human activities and composed of two mangrove species: A.marina subsp australasica, and Rhizophora

stylosa Griff. The structure of this mangrove stand is representative of New Caledonia’s mangrove main

zonation, driven by a gradient of topography and salinity: two shrubby populations of A. marina of different

ages, develop at higher elevations and salinity and form the heart itself; a band of tall R. stylosa shrubs


develops directly on the periphery of the heart, and a stratum of arborescent R. stylosa stands along

the channels. This zonation is found in all the sites of study in New Caledonia. The site of Vavouto (21°0'S,

164°42'E) is located in the Vavouto bay, in border of the ultrafamic outcrop of Koniambo, presently exploited

by Koniambo Nickel SAS for is nickel ores. Mangrove sediments downstream the outcrop’s watersheds receive

important concentrations in iron, nickel, cobalt and chromium. This export of metal toward mangroves is due

to the erosion of the outcrop during heavy rain and tropical storms, a phenomenon enhanced by the presence

of open-cast mining sites, prone to erosion. The third site, the mangrove of the FAO (21°56', 166°4’), is located

in border of two shrimp ponds, whose effluents are directly discharged within the mangrove. The zonation

and morphology differ strongly with the other sites, with trees 2 to 3 times taller than in the other sites in the

area the shrimp ponds effluents converge.

Table 1: Location and main characteristics of the sites of study. Rhizophora species include R. stylosa, apiculata, samoensis, x.lamarckii, x selala, x neocaledonica hybrid sp. Nov.

Temperate mangroves, New Zealand Semi-arid mangroves, New Caledonia

Jack Boyd,

Mangawhai

Head

Molesworth,

Mangawhai

Head

Insley,

Mangawhai

Head

Cœur de Voh, Chasseloup Bay

Vavouto, Vavouto Bay

FAO, Saint Vincent Bay

Location

estuary,

upstream

zone

estuary,

intermediate

zone

estuary,

downstream

zone

riverine frindge bay, riverine

Anthropogenic

disturbances

farm effluents, accretion process

accretion process

Control control

effluents from

lateritic outcrop

effluents from

aquaculture

farm

Sediments

characteristics muddy muddy to sandy Sandy muddy rich in Ni, Fe, Co,

Cr rich in NH4+,

OM

Mangrove

species A.marina A.marina A.marina

A.marina, Rhizophora spp.



Trees height

4-5 m

3 1-2 m 1-7 m 1-7 m 2 to 14 m


Figure 3: Satellite images of the study sites: a) Jack Boyd, Molesworth and Insley in Mangawhai Heads, North Island, New Zealand (36°S); b) the control site of “Coeur de Voh”, c) Vavouto, mining site,d)

site of FAO, impacted by aquaculture, North and South Provinces, New Caledonia (21°S).

a.

b.

c.

d.

300 km

100 km

1 km

200 m

500 m

300 m

Cœur de Voh

Vavouto

FAO

Insley

Jack Boyd

Molesworth

PGR9 – Confirmation of Candidature V1.0 Update April 26 Page 1 of 35

Methodology

-Main experimental design (illustrated in Figure 4):

• At each study site, the relative growth rate and functional traits of the trees will be measured on pre-selected

trees, chosen in circular plots of 7 m radius, following the sampling layout given in Kauffman and Donato

(2012).

• These plots will be displayed according to a systematic sampling layout, along transects perpendicular to the

water networks to analyse a potential zonation along the rivers and the intertidal gradient. These transects

will be pre-established by satellite images and disposed to maximize the area of exploration of the site. The

number of samples has been chosen to allow for multivariate and spatial analyses, and has therefore been set

to a minimum of 30 sampling points by study site (Fortin and Dale 2005).

• Growth rates and functional traits will be measured in each plot on three mature trees, here after named

“experimental trees”, labelled for further measurements. I will choose experimental trees well-exposed to

light, in order to avoid a measurement bias for functional traits sensitive to light variations. The experimental

trees will be preferably well grown and healthy individuals, unaffected by herbivores or pathogens

(Cornelissen et al. 2003).

• Leaves physiology varying according their position along the vertical gradient of the canopy, leaves functional

traits will be measured on three leaves in the understory, three in the middle tier and three in the top of the

canopy. The leaves in the understory bear indeed characteristics of shade leaves (i.e. high concentration of

Chl, lower Chla/b ratios, larger surface area and lower specific weights than leaves exposed to light [Ball and

Critchley 1982]). The leaves chosen will be fully developed emergent leaves, positioned in the upper, middle

and lower tiers of the canopy, respectively. Measurements on young leaves is preferable than on old leaves

because the latter present a higher porosity, which induce lower air flow by diffusion and therefore a lower

conductance than young leaves. Thus, young leaves record most of the gas exchange necessary for

photosynthesis (22 liters per day in average plants) and supply the adjacent tissues in air enriched in O2 by

photosynthesis (particularly roots and rhizomes, Lambers et al. 2008).

• In order to determine if the variation of relative growth rate between latitudes are due to climatic conditions

or environmental factors, the functional traits of the trees will be measured every six months (at least) during

the two first years of this research, in winter and in summer.


Figure 4: experimental design of the project aims in each site of study, and variables sampled. The double arrows represent the interactions studied for each aim.


AIM 1: INVESTIGATION OF THE RELATIONSHIP BETWEEN MANGROVE FUNCTIONAL TRAITS AND GROWTH

RATES:

Objective 1: To analyze, model and compare mangrove growth rates within different locations (different

estuaries) and geographic regions (New Zealand and New Caledonia) over two seasons (winter and summer).

• Relative growth rate is defined as “the (exponential) increase in size relative to the size of the plant present at the start of a given time interval” (Pérez-Harguindeguy et al. 2013). Allocation of biomass in different parts of the trees can vary according to environmental

constrains. On each experimental tree, I will measure the increment rate in standing biomass, during each

season, by recording:

-the relative growth rate of the main stem(s), measured along a thin line, with manual dendrometers attached

at 30 cm height (and at 130 cm height when possible). -the relative elongation of one pre-selected branch,

measured along the length of the branch, as on Figure 5.

The selection criteria for the choice of the branch are the same as for the experimental trees: the branch

should be healthy, fully exposed to light and, as far as possible, free of any herbivory or pathogens

damages.

Objective 2: To analyze, model and compare mangrove functional traits (stomatal conductance, SLA, chlorophyll and nutrient contents, and nutrient use efficiency) within different locations and geographic

regions over two seasons.

• Choice of functional traits related to growth rates: functional traits are defined here as “any well-defined morphological, physiological or phenological feature, measurable for individual plants, at the cell to the whole-organism level, and used comparatively across species, that potentially affects the organism

performance and fitness” (Mc Gill et al. 2006, Pérez-Harguinedegui et al. 2013). The following functional traits have been chosen because they are descriptive of the strategies and adaptations plants develop to

face environmental constrains, and responsive indicators of mangroves growth rate responses to environmental constrains and disturbances.

• For each tree, the height will be measured with a 16 m length telescopic stick, from the ground level to the upper boundary of the canopy, discounting inflorescences and any exceptional branches projecting above

the foliage (Cornelissen et al. 2003). In case of strongly curved trees, girth measurements will be realized along the length of the tree rather than their height, as suggested by Dahdouh Guebas and Koedam (2006).

• Diameter of Rhizophora spp. will be measured above the highest stilt root, where a true main stem exists (Clough and Scott 1989, Komiyama et al. 2005, Kauffman and Donato 2012), and at 130 cm height when pertinent.

• Wood density and carbon content of the stem and branch of the trees will be determined by the increment core technique (Figure 6). An increment core of a known length and volume will be taken on 30 trees of

different diameters and heights. The carbon content of the increment core will be determined using a

Figure 5: Branch length measurement (adapted from Pérez-Harguindeguy et al. 2013).


Shimadzu solid sample module (SSM-5000A) combined with a TOC analyzer, after grinding each core

sample for homogenization. I will then establish the relationship between wood density and diameter of the trunk in the one hand, and carbon content and diameter of the trunk in the other. These relationships

will be used to infer the average wood density and carbon content of the trees in each plot, using stem or branch diameter information.

• The volume and surface occupied by the canopy will be calculated by multiplying the length, width and

height of the canopy.

• Leaf stomatal conductance is a function of the density, size and opening of stomata, and has been shown

to be a very good indicator of leaf transpiration and CO2 acquisition (Andrews and Muller 1985). This will

be measured by means of a porometer (SC1, Decagon), by putting the conductance of a leaf in series with

two known conductance elements, and comparing the humidity measurements between them (mmol m-²

s-1, duration of measurement = 30 seconds). The measurements will be realized by dry weather to avoid a

bias due to rain, and the morning during a cloudless day, when the light is maximum and the temperature

not too high and therefore when the stomata are fully open. Measurements will be taken on the abaxial

surface of the leaves, where the density of stomata is higher and where most of the gas exchanges take

place (Figure 7).

• Chlorophyll contents in leaves will be measured by a Konica Minolta SPAD-502 meter (Osaka, Japan), a

non-destructive method which measures the difference of spectral absorbance in the red and near-

infrared regions (SPAD, unitless). The SPAD value obtained is then converted in chlorophyll a and b

contents (µg/cm²) by empirical calibrations between SPAD units and extracted chlorophyll values. We

applied here the chlorophyll extraction technique described by Hiscox and Israelstam (1979).

• I will collect 0.78 cm² (range covered by the SPAD meter disk) on 30 leaves by species (i.e. 30 for A. marina

in New Zealand, 30 for A. marina in New Caledonia and 30 for Rhizophora spp. in New Caledonia) and by

Figure 6: stem increment borer in order to establish trees age-diameter relationship

Figure 7: correct position of the porometer sensor (abaxial, left) for stomatal conductance measurements.


site, and the fresh weight will be recorded. The samples will then be immersed in a solution of acetone 80%

and kept in the dark until further measurements in the laboratory. The samples will be incubated at 65°C

until lead disks are completely colourless and the solution turns green. Chlorophyll content will then be

determined by the mean of a spectrophotometer. I will test different models of calibration curves presented

in Coste et al. (2010): the linear, polynomial, exponential and general homographic models (example Figure

8). I will then apply the most accurate model (i.e. the one presenting the minimum residual variance

determined by the minimum Aikaike Information Criterion, AIC).

Figure 8: one of the most robust models of calibration, the homographic model developed by Coste et al. (2010) based on samples collected from 13 species. The interspecific differences represent 7% of the variance. The curve equation is given by Chl = (αSPADi/(β-SPADi))+ε i , where Chl is the total content in chlorophyll a and b, SPAD the unitless reading from the SPAD-502 meter, α and β the fitted model parameters. and ε i the model residual.

Once each of these measurements are performed, these leaves will be detached from the trees.

• Specific Leaf Area (SLA) will be calculated on each leaves detached from the trees. The relationship between

leaf area and dry weight will be pre-established in each study site by measuring the ratio leaf area to leaf

dry mass on 30 leaves. The dry mass of the leaves will be measured after drying at 70°C until constant weight.

The leaves will be photographed under a glass, and their surface will be determined using software for

treatment of images, Image J. This relationship will be used to determine the SLA of each leaf detached from

the trees after measurement of their area.

• The nine leaves of the same tree will then be pooled together into a single sample, so as to obtain a triplicate for each plot. Each replicate will be dried until constant weight loss, and homogenized by crushing, until all the sample can be passed through a sieve of 0.1 mm.

• Nutrients contents in leaves: major (Fe3+, Al3+, Ca2+, Mg2+, K+, Mn2+, and Na+), minor element and trace metal

contents (Zn, B, Mo, Ni, Cd, Cu, Co, Pb, Cr), and available phosphorus (P) concentrations within leaf tissues

will be measured in nitric acid and 30% hydrogen peroxide extracts by inductively coupled plasma emission

spectroscopy (ICP-OES, Varian, Australia).

• Carbon content in leaves will be determined by the 900°C combustion catalytic oxidation method, using a

solid sample module (SSM-5000A) combined with a TOC analyzer (Shimadzu).


Total nitrogen contents will be determined by the Kjeldahl’s method, by digestion of the samples in 98%

H2SO4, to convert nitrogen in ammonia, followed by immobilization of ammonia in 7g of K2SO4, and

quantification of the ammonia by titration with a standard solution.

Objective 3: To identify the influence of mangrove functional traits on growth rates within different locations and geographic regions over two seasons (see data analyses).

AIM 2. IDENTIFICATION OF THE EFFECT OF GEOCHEMICAL AND CLIMATIC PARAMETERS ON MANGROVE

FUNCTIONAL TRAITS AND GROWTH RATES:

Objective 1: To characterize environmental variables (temperature, salinity, pH, redox potential, rain fall, nutrients) within different locations and geographic regions over two seasons.

The climatic, physical and geochemical parameters will be measured as followed:

• Location: for each plot, geographic location will be recorded with a Global Positioning System (Garmin,

GPS 60TM, USA).

• Climatic factors (soil and air temperature, light intensity above and under the canopy, relative humidity

of the air) will be measured in each plot at each sampling.

• Microtopography: topographic profile of each transect and subsequently elevation of each plot will be

estimated by the mean of a Differential Global Positioning System (DGPS).

• Redox, pH and salinity parameters: in each plot, two 50 cm depth cores will be collected with a stainless steel corer, and divided in five fractions (0-2.5cm, 5-7.5cm, 10-15 cm, 20-25 cm, 30-45 cm). The 45 cm depth has been chosen based on previous findings that most live roots in mangrove ecosystems are found

in upper 40 cm of soil layer (Alongi et al. 2005). I expect therefore that 45 cm cover most of the exchanges between the roots and the soil. One core will be kept for further analyses of nutrient contents, organic

matter, total carbon and textural analyses in laboratory. The other core will be kept for pH, redox and salinity measurements, after collecting the pore water in each fraction with a micro-samplers (Rhizon ©).

Interstitial water salinity will be measured with a hand refractometer (Atago© MASTER -S/Millα, Japan). Redox potential and pH of the pore water will be measured using a portable multimeter WTW©, provided

with an electrode of reference Pt-Ag/AgCl connected to a pH/mV/T meter. The redox values will be reported relative to a standard hydrogen electrode by adding +270 mV to the original values measured

with the reference electrode (Marchand et al. 2004, 2011a, Léopold et al. 2013).

• Major (Fe3+, Al3+, Ca2+, Mg2+, K+, Mn2+, and Na+), minor element and trace metals contents (Zn, B, Mo, Ni, Cd, Cu, Co, Pb, Cr) and available phosphorus (P) concentrations will be measured in nitric acid and 30%

hydrogen peroxide extracts by inductively coupled plasma emission spectroscopy (ICP-OES, Varian, Australia).

• The soil total carbon content (TC) will be determined by the 900°C combustion catalytic oxidation method, using a solid sample module (SSM-5000A) combined with a TOC analyzer (Shimadzu).


• Total nitrogen contents will be determined by the Kjeldahl’s method, by digestion of the samples in 98%

H2SO4, to convert nitrogen in ammonia, followed by immobilization of ammonia in 7g of K2SO4, and quantification of the ammonia by titration with a standard solution.

• Soil texture (% of sand, silt, clay) will be determined with a laser diffraction particle size analyzer (Mastersizer analyser, Malvern), after destruction of organic matter with H2O2, combined with particles dispersion with Na citrate.

Objective 2: To determine the relative and synergistic contribution of different geochemical (salinity, pH, redox potential, nutrients) and climatic (temperature, rain fall) parameters to mangrove functional traits and

growth rates (see data analyses).

AIM 3: IDENTIFICATION OF THE EFFECT OF MANGROVE FUNCTIONAL TRAITS ON LITTER QUALITY:

Objective 1: To characterize the litter quality (total organic carbon, nutrient contents, nutrient resorption efficiency, lignin content) prior decomposition within different locations (different estuaries) and geographic regions (New Zealand and New Caledonia) over two seasons (winter and summer).

• Main experimental design: the measurements of functional traits in leaves, and the monitoring of litterfall decomposition on the forest floor will be conducted in three different positions of the intertidal

gradient: upstream, in the intermediate part and downstream the estuary. In mangroves of New Caledonia, these positions are equivalent to the three main facies characteristic of mangrove species

zonation: dwarf shrubs Avicennia, small shrubs Rhizophora, and arborescent Rhizophora. In each of these three positions, the sampling and monitoring experience will be conducted in five plots, chosen among

the same plots as the ones described in the first part of this research.

• The Nutrient Resorption Efficiency (NRE) is the amount of nutrients plants can reabsorb from a leaf during the senescence, before leaf abscission. This mechanism limits the loss of nutrients in higher plants, and

is commonly encountered in ecosystems limited in nutrients, or presenting strong climatic constrains, such as a long drought or/and cold periods. This adaptation allows the plant to recycle the nutrients

already invested in leaves production. It limits therefore the allocation of energy in nutrient acquisition from the floor.

The NRE will be measured by calculating the difference between nutrients contents within green leaves and those of senescent leaves. The values used for the nutrient contents in the green leaves are the one measured in the first part of this proposal. Several senescent leaves will be sampled on the same

experimental trees, and pooled and homogenized in a single sample by tree, so as to obtain a triplicate for each plot, as previously described. Each replicate will be dried until constant weight loss, and

homogenize by crushing, until all the sample can be passed through a sieve of 0.1 mm.

Organic carbon content of senescent leaves will be measured by combustion catalytic oxidation method after each sampling. Nitrogen contents will be measured by the Kjendhal’s method, and phosphorous

content by ICP-OES after acid extraction, as previously described.


The RE will then be measured as the percentage of carbon, nitrogen or phosphorus recovered from

senescent leaves before leaf fall (Chapin and Cleve 1989 in Feller et al. 2003):

NRE (N or P, %) = N or P (mg cm-2) green leaves - N or P (mg cm-2) senescent leaves x100 N or P (mg cm-2) green leaves

• From the same nutrient measurements, I will calculate the C/N ratio, indicator of the speed of

decomposition within soils. The lower this ratio is, the faster organic matter will be decomposed by the different communities of decomposers, these communities consuming generally organic matter rich in

nitrogen for metabolic purpose. Conversely, litterfall with high C/N ratio will be decomposed slowly, leading to important humus.

Objective 2: To identify the relationship between functional traits and litter quality within mangrove stands exposed to different anthropogenic pressures and geographic regions over two seasons (see data analyses).

AIM 4: COMPARISON OF LITTER DEGRADATION RATES UNDER DIFFERENT LATITUDES AND

ANTHROPOGENIC PRESSURES

Objective 1: To identify the decomposition rate of litter fall within mangrove stands at different locations (different estuaries) and geographic regions (New Zealand and New Caledonia) over two seasons (winter and summer). • Organic matter, nutrients (C, N, P) restitution, will be monitored through a litter degradation experiment

in each mangrove subpopulation over a 180-days interval.

Whole and senescent leaves will be collected directly from the trees inside the plot, that is, yellow leaves easily detachable by a slight pulling, as in Lovelock et al. (2007). Direct collection from the trees, and not

from the ground, avoids having to wash the leaves from sediments, which could lead to nutrients leaching during the process. Leaves will be air-dried to a constant mass, labelled, and their SLA will be measured.

In each plot, five leaves (20 g approximately) of each species present in the plot will be separately placed in 20 x 10 cm nylon mesh bags, with 1mm² mesh, following Middleton and McKee (2001). The 1 mm² mesh size has been chosen to allow decomposition of the litter by fungi, bacteria and microarthropods

decomposers, while preventing consumption by macrobenthic fauna. Bags will be left on the forest floor, and attached either to a trunk or a root to avoid them to be swept away by the tide. Five leaves per

species and per plot will then be removed at 7, 14, 60 and 180 days, after which leaves will be air-dried to a constant mass and their area will be measured again. The degradation rates will be measured in

percentage of loss by day.

Objective 2: To determine the contribution of mangrove organic matter (via lignin-derived phenol ratios) in the sediment within mangrove stands at different locations and geographic regions over two seasons.


• Lignin derived phenols contents in the soil and in senescent leaves: lignin is a co-polymer of

phenylpropenyl alcohol highly abundant in plant cells, where it insures the structural integrity and minimizes water permeation of vascular plants tissues. This component is highly recalcitrant to

decomposition, and it contributes therefore to a major part of the humus on the forest floor, and increases the pool of carbon in the sediments. Lignin components play an important role in the retention of nutrients and moisture in the forest floor. In terrestrial forests, its structure increases significantly the

cation exchange capacity and the retention of water within the pore of the sediments. Thus, input of lignin in ecosystems enhance the nutrients availability, and consequently the photosynthetic capacity of

plants. The decomposition rate of lignin compounds is a function of edaphic parameters, such as oxydo-reduction potential and soil texture.

The amounts and proportion of the different lignin-derived phenols in the litter varies according to plant

sources and strategies linked with nutrients availability in the ecosystem, thus indicating the source of the organic matter found in the sediments.

I will analyze the total lignin amounts of lignin-derived compounds, and the proportions of height lignin-derived phenols in senescent leaves under different latitudes and anthropogenic pressures:

-vanillic phenols: vanillic acid, vanillic aldehyde, vanillic ketones

-syringic phenols: syringic acids, syringic aldehyde, syringic ketones -cinnamic phenols, p-coumaric and ferulic acids,

C/V ratio is defined as the molar ratio of the sum of the cinnamic phenols (p-coumaric and ferulic acids) over the sum of the vanillic phenols, and is high in sediments rich in leaf-derived compounds. The S/V

ratio is defined as the molar ratio of the sum of the syringic phenols over the sum of the vanillic phenols, and is higher in organic matter derived from woody plants, than herbaceous parts. This ratio can

therefore discriminate organic matter issued from other sources such as salt-marshes. The (Ad/Al) v ratio is the molar ratio of vanillic acid to vanillin (aldehyde). (Ad/Al) s is the molar ratio of syringic acid to

syringaldehyde. These last two ratios are higher in leaf-derived materials than in OM from wood decomposition.

These different lignin-derived phenols will be analyzed by high performance liquid chromatography, HPLC. The method used here will be adapted from Charrière (1991), and Serve et al. (1983, 1983). The

lignin-derived phenols will be oxidised by CuO-NaOH under nitrogen atmosphere at 180°C, after which the humic acids will be precipitated by acidification with HCl. The phenols will then be recovered from the extract into ethyl acetate, and analysed by HPLC.

Data analyses


AIM 1: INVESTIGATION OF THE RELATIONSHIP BETWEEN MANGROVE FUNCTIONAL TRAITS AND GROWTH RATES:

• Analysis and comparison of the seasonal patterns of mangroves growth rates within and between mangrove stands, at local and larger scales:

Variations of mangroves growth rates and functional traits in temperate and semi-arid climate between

seasons, sites and species will be compared by ANOVA crossed-factors three ways, and tested by permutations.

• Extraction and modeling of the spatial patterns of growth rates and functional traits by Asymetric Eigenvector Maps analyses (AEM):

The AEM method is an asymmetric eigenfunction-based spatial filtering method designed to model spatial

structures hypothesized to be produced by a single directional physical process (Blanchet et al. 2008a). The method allows to extract and model spatial structures of a univariate or multivariate response data set. These

spatial structures can then be used as explanative variables in linear regression to test which part of the response data sets are spatially structured at different scales (Legendre and Legendre 2012). I will analyze

separately the spatial structures of the abundances of the trees and the functional traits measured at fine scale, along the intertidal gradient, and at larger scales, between sites and latitudes. I will consider here the south-north flow of water generated during the daily tides as the physical process

likely to be at the origin of the spatial variance of the different variables, by producing chemical and/or physical gradient(s) along the estuaries.

The principle of the AEM method, fully described by Blanchet et al. (2008a, 2011, Figure 9), and the modalities of the model applied here are the following: a connection diagram between the geographic coordinates of

the samples is built, based on the graph theory diagrams or on the links one wants to consider in the analysis. This “site-by-edge” matrix is multiplied by a vector of weights corresponding to a function of the length of the

edges connecting two sites, describing the facility of exchange (of nutrients, sediments) between samples. A matrix of Euclidean distances is then computed from the weighted “site-by-edges” matrix and analyzed by

principal coordinates analysis (PCoA). The resulting principal components correspond to the AEM spatial variables.

Once these AEM spatial vectors extracted for each site of study, the variables will then be inserted into a single staggered “sites-AEM values” matrix where for each set of AEM variables, the sites belonging to another site

of study received the dummy value ”0”, following the example provided by Declerck et al. (2011, see Figure 9). These variables can then be included in a multiple linear regression model (such as redundance analysis, RDA) as explanative variables of the response data sets. The problem of collinearity induced by the large

number of AEM variables tested here will be addressed by a double procedure of forward selection (Blanchet et al. 2008b). This procedure allows to select the significant variables in each RDA, and therefore the significant

patterns of variation of the variable studied, at multiple scales.

AIM 2. IDENTIFICATION OF THE EFFECT OF GEOCHEMICAL AND CLIMATIC PARAMETERS ON MANGROVE FUNCTIONAL TRAITS AND GROWTH RATES:


• Characterization of each environmental variable along the intertidal gradient in each site of study, and test

the significances of correlations between them to highlight different habitats at different spatial scales:

-The significance of the spatial structure of each environmental variable will be tested by the statistic of

Moran (I). Their patterns will be analyzed by the function of structure of Moran (I) (correlogram) and mapping. -The relationships between each pair of variables will be analysed by ordination in a principal

components analysis (PCA) and tested by simple correlations by the mean of permutations.

• Determination of the relative and synergic contributions of the different geochemical and climatic parameters to the spatial variations of each functional trait and growth rates, in winter and in summer:

- In a first step, I will test the relationships between each response data sets (growth rates and functional traits) with the subsets of environmental parameters at all spatial scales: for each response variable,

Redundance analyses (RDA) will be performed with the subset of the soil bioavailable elements content (major and minor nutrients, trace metals), the subset of soil physic-chemical proprieties (salinity, pH, redox potential,

texture, % of total carbon) and the climatic data set (T°, PAR, humidity) to determine their respective interactions with the variation of each response variable and so, to analyze the environmental control on each

functional traits and growth rates, without regard for the spatial scales.

-After selection of the significant environmental variables by the double procedure of forward selection in each RDA, the three sets of environmental variables (bioavailable elements, physico-chemical factors, climatic

factors) will be included in two separate variation partitioning (Borcard et al. 1992, Legendre 2008). These analyses will include the different selected sets of spatial variables (the spatial AEM variables) to test the control of physico-chemical constrains, bioavailable elements and climatic conditions on the variations of the

growth rates and functional traits, at broad and fine spatial scales (Figure 10).


Figure 9: Spatial analyses of the response variables studied (growth rates or functional traits) presenting the steps of the AEM analysis with,

from top to bottom: build up and weighting of the connection diagram between the geographic coordinates of the samples based on the directional process generated by tidal flows along the estuary ; extraction of the AEM spatial variables by PCoA, followed by a forward

selection, and test of the significant spatial patterns of the variable studied in a RDA; final partition of the variation, determining at which level the variable is significantly structured; mostly at a large or medium spatial scales (fraction [a]), at a fine spatial scale (fraction [b]) or

unstructured into space, depending on very local processes or random variations.


Figure 10: Final variation partitioning for a response variable (e.g. relative growth rate of the stem). Each fraction represents the % of variation of the growth rates explained by the edaphic or/and climatic subsets of variables, along the intertidal gradient for fine spatial variation (fractions g,j,o), and between sites and latitudes (fractions e,h,k). Fractions a, b, f represent significant spatial patterns of the response variables, but unexplained by the explanatory variables choosen in the study. Fraction p represents the proportion of variance non structured into space and unexplained by the model.

- The relationships and interactions between growth rates, functional traits with the environmental conditions will be analyzed by two complementary three matrix approaches, the fourth corner (Legendre et al. 1997) and

RQL analyses (Dolédec et al. 1996, revised by Dray and Legendre 2008). Combined together, these multivariate analyses will allow us to determine which functional traits drive the growth rates at multiple scales, and in

response of which environmental factors. We will perform RQL analysis to define functional groups of species/individuals within the mangroves studied by ordination and fourth corner analysis to determine the

relationships between physiological and functional answers of the species and environmental conditions (edaphic and physical variables, and bacterial composition). The modalities of these two methods are fully

described in Brind’amour et al. (2011).

AIM 3: IDENTIFICATION OF THE EFFECT OF MANGROVE FUNCTIONAL TRAITS ON LITTER QUALITY:

• The spatial patterns of each trait of the litter quality will be extracted, tested and model by AEM analyses, in

winter and summer.

• The variation in each of these traits according to the site, season, species, age of the stand, and dominant

species in the stand will be tested by several ANOVA crossed-factors three ways.

AIM 4: COMPARISON OF LITTER DEGRADATION RATES UNDER DIFFERENT LATITUDES AND ANTHROPOGENIC PRESSURES

• Variations of litter degradation rates in temperate and semi-arid climate between seasons, species, sites and stand age, and dominant species in the stand, will be compared by several ANOVA crossed-factors three ways,

and tested by permutations.

• The relative and synergic contributions of the different geochemical and climatic parameters to the spatial

patterns of the degradation rates, at fine and larger scales, in winter and in summer, will be tested in variation partitioning, in RDA.


References (APA) Adame, M. F., & Lovelock, C. E. (2011). Carbon and nutrient exchange of mangrove forests with the coastal ocean. Hydrobiologia, 663(1), 23-50. Alongi, D. M. (1996). The dynamics of benthic nutrient pools and fluxes in tropical mangrove forests. Journal of Marine Research, 54(1): 123-148. Alongi, D. M., Ayukai, T., Brunskill, G. J., Clough, B. F., & Wolanski, E. (1998). Sources, sinks, and export of organic carbon through a tropical, semi-enclosed delta (Hinchinbrook Channel, Australia). Mangroves and Salt Marshes, 2(4), 237-242. Alongi, D. M., Wattayakorn, G., Pfitzner, J., Tirendi, F., Zagorskis, I., Brunskill, G. J.,…& Clough, B. F. (2001). Organic carbon accumulation and metabolic pathways in sediments of mangrove forests in southern Thailand. Marine Geology, 179(1), 85-103. Alongi, D. M., Clough, B. F., Dixon, P., & Tirendi, F. (2003). Nutrient partitioning and storage in arid-zone forests of the mangroves Rhizophora stylosa and Avicennia marina. Trees, 17(1), 51-60. Alongi, D. M., Wattayakorn, G., Boyle, S., Tirendi, F., Payn, C., & Dixon, P. (2004). Influence of roots and climate on mineral and trace element storage and flux in tropical mangrove soils. Biogeochemistry, 69(1), 105-123. Alongi, D. M. (2005). Mangrove–microbe–soil relations. Interactions between macro-and microorganisms in marine sediments, 85-103. Alongi, D. M. (2009). The energetics of mangrove forests. Springer: 216pp. Alongi, D. M. (2014). Carbon cycling and storage in mangrove forests. Annual review of marine science, 6, 195-219. Andrews, T. J., & Muller, G. J. (1985). Photosynthetic gas exchange of the mangrove, Rhizophora stylosa Griff., in its natural environment. Oecologia, 65(3), 449-455. Ball, M. C. (1988). Ecophysiology of mangroves. Trees, 2(3), 129-142. Ball, M. C., & Critchley, C. (1982). Photosynthetic responses to irradiance by the grey mangrove, Avicennia marina, grown under different light regimes. Plant Physiology, 70(4), 1101-1106. Barbier, E. B. (2000). Valuing the environment as input: review of applications to mangrove-fishery linkages. Ecological Economics, 35(1), 47-61. Blanchet, F. G., Legendre, P., & Borcard, D. (2008a). Modelling directional spatial processes in ecological data. Ecological Modelling, 215(4), 325-336. Blanchet, F. G., Legendre, P., & Borcard, D. (2008b). Forward selection of explanatory variables. Ecology, 89(9), 2623-2632. Blanchet, F. G., Legendre, P., Maranger, R., Monti, D., & Pepin, P. (2011). Modelling the effect of directional spatial ecological processes at different scales. Oecologia, 166(2), 357-368. Borcard, D., Legendre, P., & Drapeau, P. (1992). Partialling out the spatial component of ecological variation. Ecology, 73(3), 1045-1055.


Bouillon, S., Borges, A. V., Castañeda-Moya, E., Diele, K., Dittmar, T., Duke, N. C., ... & Twilley, R. R. (2008). Mangrove production and carbon sinks: a revision of global budget estimates. Global Biogeochemical Cycles, 22(2). Brind'Amour, A., Boisclair, D., Dray, S., & Legendre, P. (2011). Relationships between species feeding traits and environmental conditions in fish communities: a three-matrix approach. Ecological Applications, 21(2), 363-377. Clough, B. F., & Scott, K. (1989). Allometric relationships for estimating above-ground biomass in six mangrove species. Forest Ecology and Management, 27(2), 117-127. Cornelissen, J. H. C., Lavorel, S., Garnier, E., Diaz, S., Buchmann, N., Gurvich, D. E., ... & Poorter, H. (2003). A handbook of protocols for standardised and easy measurement of plant functional traits worldwide. Australian journal of Botany, 51(4), 335-380. Coste, S., Baraloto, C., Leroy, C., Marcon, É., Renaud, A., Richardson, A. D., ... & Hérault, B. (2010). Assessing foliar chlorophyll contents with the SPAD-502 chlorophyll meter: a calibration test with thirteen tree species of tropical rainforest in French Guiana. Annals of Forest Science, 67(6), 607. Dahdouh-Guebas, F., Jayatissa, L.P., Di Nitto, D., Bosire, J.O., Lo Seen, D. & Koedam, N. (2005) How effective were mangroves as a defense against the recent tsunami? Current Biology, 15: R443–R447. Dahdouh-Guebas, F., & Koedam, N. (2006). Empirical estimate of the reliability of the use of the Point-Centred Quarter Method (PCQM): solutions to ambiguous field situations and description of the PCQM+ protocol. Forest Ecology and management, 228(1), 1-18. Danielsen, F., Sorensen, M.K., Olwig, M.F., Selvam, V., Parish, F., Burgess, N.D., Hiraishi, T., Karunagaran, V.M., Rasmussen, M.S., Hansen, L.B., Quarto, A. & Suryadiputra, N. (2005). The Asian tsunami: a protective role for coastal vegetation. Sciences, 310(5748): 643. Declerck, S. A., Coronel, J. S., Legendre, P., & Brendonck, L. (2011). Scale dependency of processes structuring metacommunities of cladocerans in temporary pools of High-Andes wetlands. Ecography, 34(2), 296-305. Dittmar, T., Hertkorn, N., Kattner, G., & Lara, R. J. (2006). Mangroves, a major source of dissolved organic carbon to the oceans. Global biogeochemical cycles, 20(1). Dolédec, S., D. Chessel, C. J. F. ter Braak, & S. Champely. (1996). Matching species traits to environmental variables: a new three-table ordination method. Environmental and Ecological Statistics. 3:143–166. Donato, D. C., Kauffman, J. B., Murdiyarso, D., Kurnianto, S., Stidham, M., & Kanninen, M. (2011). Mangroves among the most carbon-rich forests in the tropics. Nature Geoscience, 4(5), 293-297. Duke, N. C. (2006a). Australia's mangroves: the authoritative guide to Australia's mangrove plants. MER. 200pp. Duke, N.C. (2006b). Mangrove biodiversity in New-Caledonia. In «Typologies et Biodiversité des mangroves de Nouvelle-Calédonie». ZoNéCo program report, pp. 213. Ellison, J.C. (1993) Mangrove retreat with rising sea-level, Bermuda. Estuarine Coastal and Shelf Science, 37: 75–87. Ellison J.C. (2005) Holocene palynology and sea-level change in two estuaries in Southern Irian Jaya. Paleogeogr Paleoclimatol Paleoecol, 220:291–309. Ellison, J. C. (2009). Wetlands of the Pacific Island region. Wetlands Ecology and Management, 17(3): 169-206.


Elser, J. J., Sterner, R. W., Gorokhova, E., Fagan, W. F., Markow, T. A., Cotner, J. B., ... & Weider, L. W. (2000). Biological stoichiometry from genes to ecosystems. Ecology Letters, 3(6), 540-550. Ertel, J. R., & Hedges, J. I. (1984). The lignin component of humic substances: Distribution among soil and sedimentary humic, fulvic, and base-insoluble fractions. Geochimica et Cosmochimica Acta, 48(10), 2065-2074. FAO (2005). Global forest resource assessment: progress towards sustainable forest management. FAO Forestry Paper 147, Rome, 348 pp. FAO, 2007. The world’s mangroves 1980-2005. FAO Forestry Paper 153. Food and Agricultural Organization, Rome, Italy. 77 pp. Feller, I. C. (1995). Effects of nutrient enrichment on growth and herbivory of dwarf red mangrove (Rhizophora mangle). Ecological monographs, 477-505. Field, C. D. (1995). Impact of expected climate change on mangroves. Hydrobiologia 295:75–81. Fortin, M. J. & Dale, M. R (2005). Spatial analysis: a guide for ecologists. Cambridge University Press. Furukawa, K., Wolanski, E., & Mueller, H. (1997). Currents and sediment transport in mangrove forests. Estuarine, Coastal and Shelf Science, 44(3): 301-310. Gedan, K. B., Kirwan, M. L., Wolanski, E., Barbier, E. B., & Silliman, B. R. (2011). The present and future role of coastal wetland vegetation in protecting shorelines: answering recent challenges to the paradigm. Climatic Change, 106(1):7-29. Giri, C., Ochieng, E., Tieszen, L.L., Zhu, Z., Singh, A., Loveland, T., & Duke, N. (2011). C. Status and distribution of mangrove forests of the world using earth observation satellite data. Global Ecology and Biogeography, 20:154-159. Harbison, P. (1986). Mangrove muds—a sink and a source for trace metals. Marine Pollution Bulletin, 17(6): 246-250. Hiscox, J. T., & Israelstam, G. F. (1979). A method for the extraction of chlorophyll from leaf tissue without maceration. Canadian Journal of Botany, 57(12), 1332-1334. Kao, W. Y., Shih, C. N., & Tsai, T. T. (2004). Sensitivity to chilling temperatures and distribution differ in the mangrove species Kandelia candel and Avicennia marina. Tree physiology, 24(7), 859-864. Kauffman, J. B., & Donato, D. C. (2012). Protocols for the measurement, monitoring and reporting of structure, biomass and carbon stocks in mangrove forests. Bogor, Indonesia: Center for International Forestry Research (CIFOR). Kerkhoff, A. J., Enquist, B. J., Elser, J. J., & Fagan, W. F. (2005). Plant allometry, stoichiometry and the temperature-dependence of primary productivity. Global Ecology and Biogeography, 14(6), 585-598. Koch, V., & Wolff, M. (2002). Energy budget and ecological role of mangrove epibenthos in the Caeté estuary, North Brazil. Marine Ecology Progress Series, 228(11). Komiyama, A., Poungparn, S., & Kato, S. (2005). Common allometric equations for estimating the tree weight of mangroves. Journal of Tropical Ecology, 21(4), 471-477. Kristensen, E., Bouillon, S., Dittmar, T., & Marchand, C. (2008). Organic carbon dynamics in mangrove ecosystems: a review. Aquatic Botany, 89(2), 201-219.


Küchler, A. W. (1972). The mangrove in New Zealand. New Zealand Geographer, 28(2), 113-129. Lacerda, L. D. (1998). Trace metals biogeochemistry and diffuse pollution in mangrove ecosystems. ISME Mangrove ecosystems occasional papers, 2, 1-61. Lambers, H., Chapin III, F. S., & Pons, T. L. (2008). Plant physiological ecology (pp. 321-374). Springer New York. Legendre, P., Galzin, R., & Harmelin-Vivien, M. L. (1997). Relating behavior to habitat: solutions to thefourth-corner problem. Ecology, 78(2), 547-562. Legendre, P. (2008). Studying beta diversity: ecological variation partitioning by multiple regression and canonical analysis. Journal of Plant Ecology, 1(1), 3-8. Legendre, P., & Legendre, L. (2012). Numerical ecology (Vol. 20). Elsevier.990pp. Leopold, A. (2012). Dynamique du carbone minéral au sein des mangroves (Doctoral dissertation, Nouvelle Calédonie). Leopold, A., Marchand, C., Deborde, J., Chaduteau, C., & Allenbach, M. (2013). Influence of mangrove zonation on CO 2 fluxes at the sediment–air interface (New Caledonia). Geoderma, 202, 62-70. Li, Ting, and Yong Ye. "Dynamics of decomposition and nutrient release of leaf litter in Kandelia obovata mangrove forests with different ages in Jiulongjiang Estuary, China." Ecological Engineering 73 (2014): 454-460. Lin, Y., Sternberg, L., & da Silveira, L. (2007). Nitrogen and phosphorus dynamics and nutrient resorption of Rhizophora mangle leaves in south Florida, USA. Bulletin of Marine Science, 80(1), 159-169. Lin, Y. M., Liu, X. W., Zhang, H., Fan, H. Q., & Lin, G. H. (2010). Nutrient conservation strategies of a mangrove species Rhizophora stylosa under nutrient limitation. Plant and soil, 326(1-2), 469-479. Lovelock, C. E., Feller, I. C., Ball, M. C., Ellis, J., & Sorrell, B. (2007a). Testing the growth rate vs. geochemical hypothesis for latitudinal variation in plant nutrients. Ecology Letters, 10(12), 1154-1163. Lovelock, C.E., Feller, I.C., Ellis, J., Schwarz, A.M., Hancock, N., Nichols, P., & Sorrell, B. (2007b). Mangrove growth in New Zealand estuaries: the role of nutrient enrichment at sites with contrasting rates of sedimentation. Oecologia 153: 633–641. Marchand, C. (2003). Origine et devenir de la matiere organique des sédiments de mangroves de Guyane française.-Précurseurs, Environnements de dépôt, Processus de décomposition et Relation avec les métaux lourds– (Doctoral dissertation, Université d'Orléans). Marchand, C., Baltzer, F., Lallier-Vergès, E., & Albéric, P. (2004). Pore-water chemistry in mangrove sediments: relationship with species composition and developmental stages (French Guiana). Marine Geology, 208(2): 361-381. Marchand, C., Disnar, J. R., Lallier-Verges, E., & Lottier, N. (2005). Early diagenesis of carbohydrates and lignin in mangrove sediments subject to variable redox conditions (French Guiana). Geochimica et Cosmochimica Acta, 69(1), 131-142. Marchand, C., Lallier-Vergès, E., & Allenbach, M. (2011a). Redox conditions and heavy metals distribution in mangrove forests receiving effluents from shrimp farms (Teremba Bay, New Caledonia). Journal of Soils and Sediments, 11(3): 529-541.


Marchand, C., Allenbach, M., & Lallier-Vergès, E. (2011b). Relationships between heavy metals distribution and organic matter cycling in mangrove sediments (Conception Bay, New Caledonia). Geoderma, 160(3), 444-456. Marchand, C., Fernandez J.M., Moreton B., Landi L., Lallier-Vergès E., Baltzer F. (2012). The partitioning of transitional metals (Fe, Mn, Ni, Cr) in mangrove sediments downstream of a ferralitized ultramafic watershed (New Caledonia). Chemical Geology, 300, 301: 70–80. McGill, B. J., Enquist, B. J., Weiher, E., & Westoby, M. (2006). Rebuilding community ecology from functional traits. Trends in ecology & evolution, 21(4), 178-185. McKee, K. L., Feller, I. C., Popp, M., & Wanek, W. (2002). Mangrove isotopic (δ15N and δ13C) fractionation across a nitrogen vs. phosphorus limitation gradient. Ecology, 83(4), 1065-1075. Mcleod, E., Chmura, G. L., Bouillon, S., Salm, R., Björk, M., Duarte, C. M., ... & Silliman, B. R. (2011). A blueprint for blue carbon: toward an improved understanding of the role of vegetated coastal habitats in sequestering CO2. Frontiers in Ecology and the Environment, 9(10), 552-560. Middleton, B. A., & McKee, K. L. (2001). Degradation of mangrove tissues and implications for peat formation in Belizean island forests. Journal of Ecology, 89(5), 818-828. Molnar, N., Welsh, D. T., Marchand, C., Deborde, J., & Meziane, T. (2013). Impacts of shrimp farm effluent on water quality, benthic metabolism and N-dynamics in a mangrove forest (New Caledonia). Estuarine, Coastal and Shelf Science, 117, 12-21. Morrisey D. J., Beard C., Morrison M., Craggs R., Lowe, M. (2007). Auckland Regional Council Technical Publication, 325; 2007. The New Zealand mangrove: review of the current state of knowledge. Mumby, P. J., Edwards, A. J., Arias-González, J. E., Lindeman, K. C., Blackwell, P. G., Gall, A., ... & Llewellyn, G. (2004). Mangroves enhance the biomass of coral reef fish communities in the Caribbean. Nature, 427(6974):533-536. Nagelkerken, I., Blaber, S. J. M., Bouillon, S., Green, P., Haywood, M., Kirton, L. G., ... & Somerfield, P. J. (2008). The habitat function of mangroves for terrestrial and marine fauna: a review. Aquatic Botany, 89(2): 155-185. Nordell, K. O., & Karlsson, P. S. (1995). Resorption of nitrogen and dry matter prior to leaf abscission: variation among individuals, sites and years in the mountain birch. Functional Ecology, 326-333. Pérez-Harguindeguy, N., Díaz, S., Garnier, E., Lavorel, S., Poorter, H., Jaureguiberry, P., ... & Cornelissen, J. H. C. (2013). New handbook for standardised measurement of plant functional traits worldwide. Australian Journal of Botany, 61(3), 167-234. Reef, R., Feller, I. C., & Lovelock, C. E. (2010). Nutrition of mangroves. Tree Physiology, 30(9), 1148-1160. Reich, P. B., & Oleksyn, J. (2004). Global patterns of plant leaf N and P in relation to temperature and latitude. Proceedings of the National Academy of Sciences of the United States of America, 101(30), 11001-11006. Robertson, A. I., & Daniel, P. A. (1989). The influence of crabs on litter processing in high intertidal mangrove forests in tropical Australia. Oecologia, 78(2), 191-198. Rönnbäck, P. (1999). The ecological basis for economic value of seafood production supported by mangrove ecosystems. Ecological Economics, 29(2), 235-252. Saenger, P., & Snedaker, S. C. (1993). Pantropical trends in mangrove above-ground biomass and annual litterfall. Oecologia, 96(3), 293-299.


Saenger, P. (2002). Mangrove ecology, silviculture and conservation. Springer. 363pp. Sakai, A., & Wardle, P. (1978). Freezing resistance of New Zealand trees and shrubs. New Zealand journal of ecology, 1, 51-61. Sakamoto, A., & Murata, N. (2002). The role of glycine betaine in the protection of plants from stress: clues from transgenic plants. Plant, cell & environment, 25(2), 163-171. Sheaves M, Brodie J, Brooke B, Dale P and others (2007) Vulnerability of coastal and estuarine habitats in the Great Barrier Reef to climate change. In: JE Johnson and PA Marshall (eds) Climate Change and the Great Barrier Reef: A Vulnerability Assessment. 1 st edition, Great Barrier Reef Marine Park Authority and Australian Greenhouse Office, Townsville, Australia, pp. 593–620. Siikamäki, J., Sanchirico, J. N., & Jardine, S. L. (2012). Global economic potential for reducing carbon dioxide emissions from mangrove loss. Proceedings of the National Academy of Sciences, 109(36), 14369-14374. Smith, T. J. (1987). Effects of light and intertidal position on seedling survival and growth in tropical tidal forests. Journal of Experimental Marine Biology and Ecology, 110(2), 133-146. Sterner, R. W., & Elser, J. J. (2002). Ecological stoichiometry: the biology of elements from molecules to the biosphere. Princeton University Press. Sukardjo, S., Alongi, D. M., & Kusmana, C. (2013). Rapid litter production and accumulation in Bornean mangrove forests. Ecosphere, 4(7), art79. Tam, N. F. Y., Wong, Y. S., Lan, C. Y., & Wang, L. N. (1998). Litter production and decomposition in a subtropical mangrove swamp receiving wastewater. Journal of Experimental Marine Biology and Ecology, 226(1), 1-18. Tomlinson, P.B. (1986). The Botany of Mangroves. Cambridge University Press, Cambridge, UK. 419 pp. Twilley, R. R., Chen, R. H., & Hargis, T. (1992). Carbon sinks in mangroves and their implications to carbon budget of tropical coastal ecosystems. Water, Air, and Soil Pollution, 64(1-2), 265-288. Virly, S, 2006. Atlas des mangroves de Nouvelle-Calédonie. In «Typologies et Biodiversité des mangroves de Nouvelle-Calédonie». ZoNéCo program report, pp. 213. Vitousek, P. M. (1984). Litterfall, nutrient cycling, and nutrient limitation in tropical forests. Ecology, 65(1), 285-298. Vitousek, P. M., & Sanford, R. L. (1986). Nutrient cycling in moist tropical forest. Annual review of Ecology and Systematics, 137-167. Woodroffe, C. D. (1985). Studies of a mangrove basin, Tuff Crater, New Zealand: I. Mangrove biomass and production of detritus. Estuarine, Coastal and Shelf Science, 20(3), 265-280. Woodroffe, C. D., & Grindrod, J. (1991). Mangrove biogeography: the role of Quaternary environmental and sea-level change. Journal of Biogeography, 479-492. Ye, Y., Tam, N. F., Wong, Y. S., & Lu, C. Y. (2003). Growth and physiological responses of two mangrove species (Bruguiera gymnorrhiza and Kandelia candel) to waterlogging. Environmental and Experimental Botany, 49(3), 209-221. Zann LP., Vuki V. (2000). The southwestern Pacific Islands region. In: Sheppard CRC (ed) Seas at the millenium: an environmental evaluation. Permagon, Amsterdam, 705–722.


Ethical Approval The thesis does not report on research involving animals, humans, human biological materials, nor the creation of human gametes, human or hybrid embryo. It does not involve issue related to health nor disability.

By consequent, the proposed research does not require an ethical approval.

Resources and Budget Underlined amounts will be funded by the PhD AUT research funding, the rest by the budget lines of the Institute of Research for the Development (IRD) of New Caleodnia.

BUDGET FORM

ITEM COST ($) Total

Y1 (PhD)

Y2 (PhD)

Y3 (PhD)

Travel

Round trip Nouméa (New Caledonia)-Auckland 750 750 750

Accommodation for fieldwork in New Zealand 300 650 300

Accommodation for fieldwork in New Caledonia 300 650 300

Instruments

Litter bags1 mm mesh 150 0 0

Decametre, measuring tapes 140 0 0

Manual dendrometers D1 0 250 0

Stainless steel core (1m length) 90 0 0

leaf porometer (SC1©) - - -

Konica Minolta SPAD-502 meter (Osaka, Japan) - - -

LI-COR© LAI 1400 - - -

Laboratory equipment usage charges

inductively coupled plasma emission spectroscopy_ICP-OES analyse in AUT and IRD

0 2710 600

Laser Granulometer for textural analyses 200 200 0

HPLC for Lignin analyses, (Medium usage: 31-200 samples =350$) and method development

0

650 0


Laboratory chemicals

ICP-OES: nitric acid 70% (2.5L), 30% hydrogen peroxide(1L) 0 500 300

Chemicals for nitrogen analyses: H2SO4 FW=98.08(2.5L), K2SO4 98% 0 400 200

Chemicals for lignin analyses: NaOH 2N, CuO, ammonium iron II sulphate, HCl 37%, FW=36.46

(2.5L), ethylether, pyridine, Helium 0 350 0

Chemicals for textural analyses of samples with laser granulometer: KMnO4 (500g=5600XPF),

H2O2 30% (2.5L = 6500XPF) 0 200 0

Chemicals for leaves chlorophyll contents, by spectrophotometry: acetone 80% FW=58.08,4L 0 300 0

Conference (if appropriate)

300 500

Consumables

Consumables for laboratory (vials, filters…) 150 350 300

Consumables for fieldworks (leaves and soil sampling bags, markers)

80 80 0

TOTAL 4860 8340 3250 16 450

TOTAL funded by the AUT PHD budget 740 6560 1700 9 000

TOTAL funded by the IRD budget 4120 1780 1550 7450

Location -Fieldwork on temperate mangroves of New Zealand will be realized in the North Island of New Zealand, in Mangawhai Estuary. All analyses in laboratory on the soil (major and minor nutrients, N and C concentrations, textural analyses) and leaves (major and minor nutrients, N and C analyses, lignin concentrations) samples collected in New Zealand will be analysed at the Auckland University of Technology. -Fieldwork on semi-arid tropical mangroves of New Caledonia will be repeated following the same methodologies in the North Province (sites of Voh and Vavouto) and the South Province (site of Saint Vincent Bay). All the analyses on soil and leaves samples collected in New Caledonia will be repeated with the support of the Research Institute for the Development (IRD) of Nouméa, and conducted in the laboratory of Cyril Marchand (IRD, 206 - Institute of Mineralogy and Physics of Condensed Media, IMPMC), second supervisor of this research.


Progress and Activity to Date • Bibliographic researches and literature review.

• Realization of a six month pilot study in New Caledonia to evaluate the feasibility of the methods and

develop protocols. During this pilot study, the following points have been achieved:

-development of the experimental design, - labeling of experimental trees within each plots in the three sites,

- first sampling season for the soil cores and functional traits in the three sites of study (sampling of the leaves, stomatal conductance, chlorophyll measurements, SLA, morphological traits), - measurements of standing biomass by plots in the three sites of study,

- SPAD and Chlorophyll regression models in New Caledonia, - development of the textural analyses protocol by laser granulometer and training,

- development of the total organic carbon analyses protocol by the 900°C combustion catalytic oxidation method, and training.

• Establishment of sampling plots in sites in New Zealand.


Timetable for Completion

Time in periods of 2 months

Analyses of sediments, leaves tissues and microalgae (ICP, texture, C/N, Chla, isotops, fatty acids)

Year 1 Year 2 Year 3

Jl-A S-O N-D J-F M-A M-J Jl-A S-O N-D J-F M-

A M-J Jl-A S-O N-D J-F M-A M-J

Literature review

PGR9 preparation

Fiel

dwor

ks

Measure and monitoring of mangroves growth rates and functional traits

NZ NC NZ NC NZ NC

Soil cores collections within sites NC NC NC NZ NC NZ

monitoring of litter degradation in litter bag experiment

NC NZ NC NZ

Anal

yses

in La

bora

tory

Analyses of leaves nutrient contents, SLA NC NC NZ NZ NZ NZ NZ

Analyses of soil texture, organic carbon contents NC NC NC NC NC

Analyses of soil nutrients contents (Kjeldhal’s method, ICP-OES)

NZ NZ NZ NZ NZ

Analyses of lignin compounds NZ NZ NZ NZ NZ

Data analyses

Write-up of Papers

Thesis Write-up


Publications and Presentations

D E C L A R A T I O N B Y A P P L I C A N T

I declare that the information provided by me in this application is true and complete. I have read and understand the conditions of candidature outlined in the current Postgraduate Handbook and am prepared to accept them in full. This proposal has been discussed between my supervisors and myself and I therefore submit it for confirmation of my candidature.

Applicant’s signature: Date:

form pgr9 confirmation of candidature research proposal ... fileform pgr9 confirmation of...

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