Late spring decomposition rates in a second order

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L’uomo nell’ecosistema:

una relazione bilanciata?

XIX Congresso della Società Italiana di Ecologia

“Dalle vette alpine alle profondità marine”

Bolzano, 15-18 settembre 2009

Volume 1

Roberta Bottarin, Uta Schirpke, Ulrike Tappeiner

in collaborazione con la Società Italiana di Ecologia

2010

Contenuto

Editorial advisors 9

Prefazione 10

Introduzione 11

Tavole Rotonde 13

Ecosystem Services Partnership, verso la costituzione di un gruppo italiano (Alessandro Gretter) 15

Ecologia e produzione idroelettrica a confronto (Luca Dal Bello & Bruno Maiolini) 19

Pattern spaziali e processi ecologici 23

Analisi della biodiversità nell’Ecoregione mediterranea: sinergia fra ricerche in campo ed analisi satellitari (Roberto Cazzolla & Claudia Notarnicola) 25

Decomposizione della lettiera di quattro specie della macchia mediterranea: relazioni con alcune caratteristiche fogliari e con la qualità della lettiera (Anna De Marco et al.) 37

Late spring decomposition rates in a second order stream: assessing relationships among breakdown rates, decomposer diversity and substrate morphology (Gina Galante et al.) 45

An instrument to assess the agro-ecological value of the Lombardia plain (Northern Italy) from land-cover cartography: preliminary results (Marta Maggi et al.) 59

Alpine-wide delineation of the potential treeline (Caroline Pecher et al.) 71

Effetti della ricchezza specifica, delle abbondanze relative e della taglia corporea sul processo di decomposizione fogliare in microcosmi di laboratorio: quanto contano realmente le specie? (Angela Pluchinotta et al.) 77

A preliminary analysis of GIS-based Decision Support System to monitor climate aridity and drought in Mediterranean area (Luca Salvati et al.) 89

Cartografia ad alta risoluzione della connettività e stima dell’effetto barriera: una metodologia basata su parere esperto e immagini LiDAR (Rocco Scolozzi & Daniele Vettorato) 97

Premio Marchetti – Wolf prey selection and food availability in the multi-prey ecosystem of Majella National Park, Abruzzo (Azzurra Valerio et al.) 105

6

Impatto antropico: effetto di disturbo o di controllo? 121

The evaluation of tourism ecological footprint: a comparison

between Italy and Germany (Roberta Aretano et al.) 123

Pressioni antropiche e stato ecologico del Lago di Pusiano:

nuove prospettive (Elisa Carraro et al.) 131

La capacità di carico come strumento di supporto alla pianificazione

in ambito turistico (Valentina Castellani & Serenella Sala) 139

Sostenibilità e bioenergia: un’applicazione del modello CO2FIX ai boschi

della Lombardia (Giulia Fiorese et al.) 149

La realtà dei commons in Trentino e Cumbria: Governance sostenibile

e resilienza dei sistemi socio-ecologici (Alessandro Gretter & Rocco Scolozzi) 159

Andamento delle fioriture di cianoficee nel lago Trasimeno (1992-2007)

(Rosalba Padula & Linda Cingolani) 169

Risposta fotosintetica di alcune specie macroalgali in ambiente acidificato

(Lucia Porzio) 181

Towards the definition of a “Zone of Interaction” of protected areas

to quantify and monitor the human impact on biodiversity

(Francesco Rovero & Ruth DeFries) 189

The effectiveness of different management policy to support

the Natural Capital (Teodoro Semeraro et al.) 197

The integrated information system on water supply and wastewater

services: the Italian experience in the urban water survey

(Stefano Tersigni et al.) 205

The management of the marinas in the context of environmental security

(Donatella Valente et al.) 213

Come apprezzare i “servizi” offerti dagli ecosistemi? 221

Il valore delle funzioni del bosco nella percezione delle comunità locali:

un caso di studio nel comune di Trento (Maria Giulia Cantiani et al.) 223

Stima dei servizi ecosistemici a scala regionale come supporto a strategie

di sostenibilità (Maria Angela Cataldi et al.) 231

Verso una mappatura dei servizi ecosistemici in ambito turistico alpino,

caso della Val di Ledro (TN) (Alessandro Gretter et al.) 241

Ricostruzione nell’Orto Botanico di Napoli di un ambiente

lagunare (mangrovieto) delle aree costiere tropicali di Veracruz, Messico

(Bruno Menale et al.) 249

Turismo balneare e percezione dei servizi ecosistemici nel Parco Naturale

Regionale “Litorale di Ugento” (Lecce, Italia) (Nicola Zaccarelli et al.) 259

7

Educazione ambientale oggi 269

Approccio locale all’educazione ambientale: lezioni da due esperienze in ambiente mediterraneo (Lucia Fanini et al.) 271

Una guida all’osservazione e allo studio dei microrganismi acquatici (Annastella Gambini et al.) 279

Ecologia in laboratorio: l’esperienza dei Microcosmi Acquatici (Annastella Gambini et al.) 289

Consumi amici del clima: capire i cambiamenti climatici facendo di conto (Giovanna Ranci Ortigosa et al.) 297

Ecologia: raccontami la storia del mio futuro (Serenella Sala & Valentina Castellani) 307

Autori 315

45

Late spring decomposition rates in a second order stream: assessing relationships

among breakdown rates, decomposer diversity and substrate morphology

Tassi di decomposizione tardo primaverili in un fiume di secondo ordine:

studio delle relazioni tra decomposizione, biodiversità dei decompositori

e dimensione frattale del substrato

Gina Galante1*, Biancamaria Pietrangeli2, Oriana Maggi1, Rossana Cotroneo3, Silvia Panetta1, Domenico Davolos2 &

Edoardo Scepi1

1 Dept. of Plants Biology, University of Rome “Sapienza”, Piazzale Aldo Moro 5, 00185 Rome 2 ISPESL-DIPIA, Via Urbana 167, 00184 Rome

3 ISTAT, Environmental Dpt., Via Cesare Balbo 16, 00184 Rome*[email protected]

Abstract

Ecological processes are inluenced both by biotic and abiotic factors. Leaf litter breakdown in freshwater does not except this rule. Substrate morphology and characteristics may inluence benthic decomposers abundances and distribution. In fact, both substrate and macroinvertebrates follow a patchy distribution along stream’s ecological gradients. Abundances and diversity of decomposers directly afect leaf litter processing and decomposition rates linking biotic and abiotic factors to the ecological process of decomposition. Fractal dimension of substrates can give a measure of surfaces complexity and may be related both to water turbulence and macroinvertebrates clinging. In this study we have investigated the functional relationships between macroinvertebrates diversity and abundances, chemical and physical parameters, rocks, pebbles and stones numbers and dimension and fractal dimension of substrate.

Gina Galante et al.

46

Introduction

Decomposition of organic matter is a continuous process involving biotic (decomposers and detritivores) and abiotic factors such as physical abrasion, sub-strate characteristics, physical and chemical water conditions. herefore, these inter-acting factors inluence the distribution of benthic organisms and are indirectly related to decomposer diversity and abundances. Environmental heterogeneity (Wright & Li, 2002) at a variety of scales is often inluenced to a greater extent by local or small-scale heterogeneity (Archambault & Bourget, 1996; Bertness et al., 1996; Wright & Li, 2002), resulting in more patchy distributions of benthic macro fauna. Small-scale heterogeneity includes size, distribution and surface texture of substrates constituents (rocks, stones and pebbles), that can inluence both produc-tivity due to the availability of refuges and food and storage capacity of substrate (Jefries, 1993; Cardinale et al., 2002, 2006) and physical fragmentation of organic matter, because of the increment of water turbulence and oxygenation caused by its heterogeneous surfaces (Melillo et al., 2004) Fractal geometry is now generally used to describe the surface texture of substrates in freshwater benthic studies: therefore fractal methods were used to describe textural diferences in constructed substrates with a checkerboard arrangement of heights (Taniguchi & Tokeshi, 2004), to esti-mate the fractal dimension of riverbed topography (Robson et al., 2002), and to de-scribe the substrate-water interface of streambeds. In this study we have analysed all the possible relationships occurring between macroinvertebrates diversity and abun-dances, substrate morphology and decomposition rates in a second order stream, taking into account the main chemical and physical parameters. he comprehension of the principles that link biodiversity, ecological processes and morphology as well as features of substrates could also improve new management systems of the freshwa-ter resources and the assessing of new stream health indicators.

Late spring decomposition rates in a second order stream

47

Methods

Study area

he river Sacco is located in the south-east of Latium (Italy), it lows along 84 km with an average slope ranging from 0.2 to 2.0 %. he headwater has an alti-tude of 226 m a.s.l., climate of this zone is properly of Mediterranean type with very rainy winter and spring and summer dough. River springs are still pristine while in the loodplain area there are strong pollution impacts due to industrial installation and urban discharge. River bed and substrates characteristics are heterogeneous and patchy with sandy areas and pebbly and stony zones. he riparian forest is mainly constituted by Alnus glutinosa L. (dominant), Salix alba L., Populus tremula L. and Populus nigra L. he stream channel in the river springs zone is partially shaded with some more lighted zone corresponding to conining crop cultivations. Water depth in river source ranges from 20 cm to 1 m due to the presence of sandy patch pools that can be deeply excavated during river lood. Width channel ranges from 2.5 to 3.0 m, river order range from two to three. hree sampling sites were selected in headwater area along 5 km stretch of the river. Each sampling station had 100 m length and showed homogeneous riverbed characteristics.

Field procedures

his study started on 21st May 2009 using litter bags technique and run over 3 weeks. Alder (Alnus glutinosa L.) autumn leaves were collected just before abscis-sion, stored air-dried, weighted into 3 gram (+/– 0.001) groups and placed in ine mesh (Graça et al., 2007) (0.2 mm, Esthal-Mono, Sefar) and coarse mesh (5 mm 10 x 15 cm) bags. Initial mass was corrected for both manipulation and humidity losses (Graça et al., 2007). A total of 150 bags were sealed and randomly distributed at the sampling sites. Coarse and ine mesh bags were positioned both in riles and pools areas, well submerged, tied to rocks and stones with ishing nylon wire. Dis-solved oxygen and water temperature were relieved at each sampling station and bag positioning point. Geographic coordinates were assigned to each bag. Triplicate of ine and coarse mesh bags were retrieved from each sampling site weekly.

Gina Galante et al.

48

Leaf mass loss estimation

he sampled litter bags were placed individually in plastic bags and then brought to laboratory. Leaves were removed from bags, rinsed with deionized water to remove sediments and adhering invertebrates. Leaf material was dried at 60 °C to constant mass for 72 h, weighted, burned in mule furnace at 500 °C for 6 h and weighted again at the nearest 0.01 (Graça et al., 2001; 2007). Leaf breakdown rate (k) was estimated by itting the amount of remaining leaf material data to the expo-nential model, Yt = Y0 e-kt, where Yt is the AFDM remaining at time t (days), and Y0 the AFDM at the beginning of the experiment (Petersen & Cummins, 1974). Leaf mass losses were estimated for each litter bag using the relation ln ( Yt/Y0) = – Kt. In decomposition rates the curve itting AFDM was expressed as percentage of the re-mains mass.

Biodiversity analysis

he leaves in coarse mesh bags were rinsed into a 400 µm mesh screen to re-tain the associated macroinvertebrates, which were sorted and collected in ethanol (70 % v/v) until identiication and counting. Macroinvertebrates were identiied by stereomicroscope to family, except for oligochaeta that were identiied at genus level in according to Merrit & Cummins (1996). SHDI (Shannon Diversity Index), SHEI (Shannon Evenness Index), S-species richness and total abundances per bags, collecting data and sampling station were determined. hree replicates of ine mesh bags were sent to microbiology laboratory to asses microbial and fungal diversity. he estimation of microbial community composition was performed by genetic in-gerprinting techniques. Scanning laser microscopy was used to examine the charac-teristics of the bacterial strains. Isolated bacteria were identiied by PCR ampliica-tion and sequencing. Phylogenetic analyses were conducted for inferring the evolutionary relationships of the examined taxa.

Substrates characteristic and Fractal Dimension estimation

Substrate characteristics were detected by overlaying a one meter wide plastic square upon each litter bag (litter bag in the geometric centre) and counting number and dimensions of stones, rocks and pebbles inside the square sediment characteris-tic were detected as well (gravel, sand or a mix of the two), then substrate selected


49

areas were photographed. Five classes of rocks, stone and pebbles: rocks > 25 cm;

rocks = 20 cm; stones = 15 cm, pebbles = 10 cm and number of pebbles/cm2 were

identiied. he acquired images were imported in ArcGIS software and transformed

into grid formats. In the following step, images were processed to eliminate water

relex, exported as bitmap format and elaborated with Fractal 3 software to calculate

fractal dimension (FD) in gray scale using box-counting method, a quantitative

analysis of perimeter convolution to evaluate the degree of roughness of input im-

ages. Commonly known as the Hausdorf Dimension (H.D.), the algorithm is

Eq. (1)

and gives the aggregate perimeter roughness as a fractal dimension. he fractal di-

mension describes the complexity of an object (Carr & Benzer, 1991) (Fig. 1).

Procedure for acquisition Figure 1:

and elaboration of substrate fractal

dimension:

1: plastic square positioned on the

studied area;

2, 3, 4: elaboration steps. In the

bottom two graphical examples

of box counting method to

calculate FD.

Statistical analysis

Diferences in decomposition rates between ine and coarse mesh bags in the

three sampling stations were analysed by ANOVA, such as diferences in macroin-

vertebrates number of individuals and taxa, both for sampling date and station. A

correlation matrix was elaborated to relate inal AFDM to: substrate characteristics,

fractal dimension of substrate (FD), SHDI, SHEI, S-species richness, total abun-

dances, dissolved oxygen and water temperature. he same parameters, calculated

per bags, were used to perform a CCA (Canonical Correspondence Analysis). Before

proceeding to apply the CCA model a data pre-processing to reduce redundant in-

Gina Galante et al.

50

formation was executed. In this paper, the variables selection was carried out across the statistics techniques of the stepwise algorithm: an heuristic method that exam-ines variables, according to the well known “parsimony principle” (“entia non sunt multiplicanda praeter necessitatem”, or “entities should not be multiplied beyond necessity”).

Results

During the experimental period water temperature ranged from 16.0 to 13.2 °C. During the 1st week water temperature ranged from 14.0 to 15.5 °C while in the 2nd week there was an abrupt water temperature drop due to meteorological condition. In the 3rd week gradually water temperature increased until reaching the same values recorded in the irst week. Decomposition process was quite complete in three weeks. Breakdown rates of coarse mesh bags resulted to be: K = –0.0961 d –1 K = –0.1183 d –1 and K = –0.1056 d –1 respectively in sampling stations 1, 2 and 3. No statistically signiicant diferences were found comparing K among the three sampling stations for both ine and coarse mesh size litter bags. Statistically signii-cant diferences were found between coarse and ine litter bags decomposition rates (Tab. I).

ANOVA results for differences between fine and coarse mesh size decomposition rates.Table I:

Effect Univariate Tests of Significance, Effect Sizes, and Powers for k (matrice k fine-coarse.sta)

SS Degr. of Freedom

MS F p Partial eta-squared

Non- centrality

Observed power (alpha = 0.05)

Intercept 0.286786 1 0.287 20.95 0.000 0.344 20.950 0.994

Mesh-size 0.078070 1 0.078 5.70 0.022 0.125 5.703 0.645

Site 0.015444 2 0.008 0.56 0.573 0.027 1.128 0.137

Error 0.547572 40 0.014

he bacterial activity contributes to process for almost 40 %. On the basis of the 16S rRNA gene sequences analysis the isolated bacteria belonged to the follow-ing taxa: Serratia sp., Aeromonas sp., Citrobacter sp., Ochrobactrum sp., Flavobacteri-

um sp., Duganella sp., Acinetobacter sp., Stenotrophomonas sp., Pseudomonas sp., Ba-

cillus sp., Flavobacterium sp.,Rheinheimera sp., Agrobacterium sp. (Fig. 2).


51

Phylogenetic affiliations of the bacteria relived from leaf material (highlighted in Figure 2:

boldface). The tree was constructed by the NJ method, the nucleotide substitution rates were

calculated by using Kimura’s two-parameter model; only values >50 % are displayed.

All identiied bacteria are those characteristic of decomposition processes in

freshwater. Sampling station 1 showed the lower breakdown rate while the faster K

was recorded at station 2. For those who regard macroinvertebrates analysis 18 dife-

rent families were counted and the main biodiversity indices were elaborated (Tab.

II, III).

Gina Galante et al.

52

Identified families.Table II:

Taxa st1 st2 st3

Baetidae 41 123 83

Habropheliae 26 17 23

Heptageniidae 0 4 0

Leptophlebiidae 5 15 0

Caenidae 22 14 29

Ceratopogonidae 27 14 9

Chironomidae 8 3 12

Simulidae 10 14 29

Muscidae 1 0 0

Tupilidae 0 0 1

Culicidae 3 0 0

Rhyacophylidae 3 1 3

Polycentropodiae 21 13 8

Tubificidae 25 7 47

Lumbricilidae 1 0 0

Nemouridae 4 0 12

Anfiphipodae 1 0 2

Nepidi 0 0 1

Diversity indices.Table III:

st1 st2 st3

Shannon-Wiener Diversity Index 2.2872 1.6501 2.0206

Species Richness (S) 16 11 13

Total Abundance 199 225 259

Simpson Diversity Index 0.1228 0.3254 0.1752

Evenness 0.8249 0.6881 0.7877

Shannon Entropy 3.2997 2.3807 2.9151

Some signiicant diferences in species distribution and abundances were highlighted. Baetidae family (Ephemeropthera, ghatering collectors, scrapers) resulted the most representative group in terms of relative abundance. Fluctuations in species composition and relative abundances are showed in table IV and igure 3.


53

Differences in species order’s relative abundances. Ephemeropthera, and Tricoptera Table IV:

show significant statistical variation in abundances. A more severe control of variance differen-

ces highlights a reasonable difference among sampling stations just for ephemeropthera

(Newman-Kauls test).

Effect

Multivariate Tests of Significance

Sigma-restricted parametrization

Effective hypothesis decomposition

Test Value F Effect ERROR p

Intercept Wilks 0.089 23.905 9 21.000 0.000

Sampling date Wilks 0.116 4.505 18 42.000 0.00003

Sampling station Wilks 0.164 3.422 18 42.000 0.0005

Date*Station Wilks 0.034 3.272 36 80.434 0.000005

Cell

No.

Newman-Keuls test; Ephemeropthera, alpha = 0.05

Error: Between MS = 59.667, df = 29.000

Date Station Ephemeropthera 1 2 3

4 2009/06/04 1 0.33333 ****

5 2009/06/04 2 3.00000 ****

9 2009/06/11 3 4.09091 ****

6 2009/06/04 3 7.66667 ****

7 2009/06/11 1 8.71429 ****

8 2009/06/11 2 11.00000 ****

1 2009/05/28 1 17.00000 **** ****

3 2009/05/28 3 26.00000 ****

2 2009/05/28 2 48.33333 ****

Relationship among factors (sampling location and date) and taxa abundances. Figure 3:

A, tricopthera; B, ephemeropthera.

Gina Galante et al.

54

Relationships among macroinvertebrates decomposers diversity indices, AFDM, temperature, dissolved oxygen, substrate characteristics and fractal dimen-sion (FD) were highlighted by correlation matrix: both total abundances and AFDM are correlated to fractal dimension of substrate. Substrate FD resulted negative cor-related both to the number of big rocks > 25 cm and to the evenness. he presence of big rocks seems to reduce FD. Finally AFDM resulted to vary in function of FD, pebbly substrate and total abundances (Tab. V).

Correlation matrix for quantitative data. Relationships among biotic and abiotc Table V: variables.

Variables

Marked correlation are signiicant at p <.05000

AFDM

Ox m

g/L

T °C

Rock

s > 2

5

Rock

s = 2

0 cm

Ston

es =

15

cm

pebb

les =

10

cm

pebb

les x

10

cm2

Subs

trat

e Fr

acta

l

Dim

ensi

on

SHDI

S, sp

ecie

s

richn

ess

SHEI

Tota

l

abun

danc

es

AFDM 1.000 0.130 –0.240 –0.161 –0.073 –0.250 0.194 0.329 0.423 0.066 0.172 –0.043 0.514

Ox mg/L 0.130 1.000 –0.533 0.133 0.343 –0.030 0.342 0.367 –0.111 –0.126 –0.063 –0.138 –0.030

T °C –0.240 –0.533 1.000 0.097 –0.179 0.138 –0.208 –0.300 0.060 –0.158 –0.087 –0.072 0.165

Rocks >25 –0.161 0.133 0.097 1.000 0.161 0.160 0.311 0.315 –0.362 –0.106 –0.103 0.013 –0.140

Rocks = 20 cm –0.073 0.343 –0.179 0.161 1.000 0.383 0.374 0.161 –0.036 –0.047 –0.050 –0.135 0.039

Rocks = 15 cm –0.250 –0.030 0.138 0.160 0.383 1.000 0.318 0.134 –0.065 0.161 0.088 0.126 0.072

pebbles = 10 cm 0.194 0.342 –0.208 0.311 0.374 0.318 1.000 0.559 0.058 0.203 0.211 –0.035 0.246

pebbles x 10 cm2 0.329 0.367 –0.300 0.315 0.161 0.134 0.559 1.000 0.061 0.099 0.050 0.093 0.089

Substrate Fractal

Dimension0.423 –0.111 0.060 –0.362 –0.036 –0.065 0.058 0.061 1.000 –0.227 –0.048 –0.367 0.367

SHDI 0.066 –0.126 –0.158 –0.106 –0.047 0.161 0.203 0.099 –0.227 1.000 0.849 0.742 0.253

S, species richness 0.172 –0.063 –0.087 –0.103 –0.050 0.088 0.211 0.050 –0.048 0.849 1.000 0.383 0.556

SHEI –0.043 –0.138 –0.072 0.013 –0.135 0.126 –0.035 0.093 –0.367 0.742 0.383 1.000 –0.076

Total abundances 0.514 –0.030 0.165 –0.140 0.039 0.072 0.246 0.089 0.367 0.253 0.556 –0.076 1.000

FD of substrate ranged from 2.189 (station 1) to 2.682 (station 3). CCA analysis (AFDM target variable) highlights for the irst axe a high statistically signii-cant value for total abundances and pebbles/10 cm2, thus conirming the correlation matrix results (Fig. 4). In particular, total abundances are positively correlated, while pebbles are negatively correlated. AFDM decreased at the total abundances increase and in presence of pebbly substrate. With regard to the second axe, it was found that stones = 15 cm, fractal dimension of substrate and temperature are negatively corre-


55

lated with AFDM, while total abundances and pebbles are positively correlated.

Decrements of both temperature and fractal dimension of substrate inluence the

decomposition process. In other words, low temperature and low fractal dimension

of substrate inhibit the decomposition process.

CCA results: Figure 4:

A - dimension1 vs dimension3,

B - dimension 1vs dimension 2

Discussion

Although relationships among macroinvertebrates temperature and leaves

breakdown rates are already known since long time (Cummins, 1974) the connec-

tion between decomposition process and fractal dimension of substrates are not yet

properly investigated. he relationship among spatial heterogeneity of substrate (pat-

tern) and macroinvertebrates assemblage in a stream ecosystem were investigated in

a recent study (Boyero, 2003), but the fractal dimension of substrate was not taken

into account, and the efects of diferent patterns on litter breakdown processes were

ignored. Technical diiculties to detect substrates fractal dimension in situ, mainly in

an aquatic environment, have often limited researches in this ield. he new method-

ology developed in the present study makes easier the evaluation of this parameter.

he results of this work highlights new aspects connecting directly the breakdown

Gina Galante et al.

56

rates to the complexity and heterogeneity of substrate structure, and in particular makes possible to establish the type of substrate structure that more inluence de-composition. Low FD values were found in those substrates constituted by big stones (> 25 cm) and, although this kind of substrate promotes water turbulence and oxygenation, it resulted negatively correlated with leaves breakdown rates. We can argue that leaves’ physical fragmentation and high water oxygenation enhanced by big stones does not promote an increasing of decomposition rates. Otherwise, sub-strates constituted by small rocks and pebbles smaller than 10 cm showed bigger FD, and contributed to increase processing rates. Besides being connected to breakdown rates, substrate FD resulted negatively connected to the SHEI index (higher SHEI index in presence of big rocks). his study suggest that an high value of FD can inluence both species abundance and decomposition rates: probably due to its con-tribution to the availability of refuges, hanging and feeding surfaces for inverte-brates, but also because of the increased properties of substrate retention (Jefries, 1993). he more complex a substrate is, the more abundant are species (in this case ephemeropthera) and faster K. he heterogeneity of and abiotic factor seems to inluence directly an important ecological process as decomposition. Environmental heterogeneity (Wright & Li, 2002) at a variety of scales is often inluenced to a greater extent by local or small-scale heterogeneity (Archambault & Bourget, 1996; Bertness et al., 1996; Wright & Li, 2002). herefore, it could be interesting to inves-tigate more deeply substrate FD (characterized by scale invariance properties) and its relation with breakdown rates at diferent spatial scales, to highlights the hierarchical dominie of FD change. he knowledge of the level at which substrate heterogeneity ceases to inluence the decomposition process could be also useful for the river eco-logical management.


57

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Late spring decomposition rates in a second order