دوماه نامه

نوع مقاله : مقالات پژوهشی

نویسندگان

1 دانشگاه رازی کرمانشاه

2 دانشگاه صنعتی خاتم الانبیاء بهبهان

چکیده

با توجه به نقش مهم ماکروفون خاک در عملکرد اکوسیستم، تعیین فاکتورهای تأثیرگذار روی توزیع این جانداران خاکزی از اهمیت زیادی برخوردار است. این تحقیق جهت بررسی ارتباط مکانی تنوع زیستی جانداران درشت خاکزی و تاج پوشش درختان در جنگل های حاشیه رودخانه مارون انجام گرفت. جانداران خاکزی با استفاده از 175 نقطه نمونه به ابعاد 50 سانتی متر *50 سانتی متر و در عمق 10- 0 سانتی متر به روش دستی، روی ترانسکت هایی موازی با فاصله 100 متر از یکدیگر و عمود بر رودخانه، نمونه برداری شدند. فاصله قطعه نمونه های ماکروفون از یکدیگر بر روی ترانسکت ها 50 متر انتخاب گردید. همچنین درصد تاج پوشش کل و درصد تاج پوشش پده، گز و سریم در قطعه نمونه هایی به ابعاد 5 متر × 5 متر مورد اندازه گیری قرار گرفت. شاخص های تنوع Shannon H، یکنواختی Sheldon و غنای Menhinick در مورد جانداران خاکزی محاسبه شدند. سپس متغیرهای مورد نظر با استفاده از روش های زمین آماری مورد تحلیل قرار گرفتند. تاج پوشش کل، پده، سریم و شاخص های تنوع زیستی جانداران خاکزی دارای الگوی توزیع مکانی مشخص با مدل نمایی بودند. دامنه تأثیر شاخص های تنوع زیستی جانداران خاکزی و تاج پوشش کل و پده نزدیک به هم و ساختار مکانی این متغیرها یکسان و به صورت مدل نمایی است. تحلیل همبستگی نیز ارتباط مثبت ویژگی های مورد بررسی را تأیید می نماید. بنابراین می توان اظهار داشت که تغییرپذیری مکانی تاج پوشش درختان و بویژه گونه پده، الگوی پراکنش تنوع جانداران خاکزی را تحت تاثیر قرار می دهند.

کلیدواژه‌ها

عنوان مقاله [English]

Spatial Structure of Soil Macrofauna Diversity and Tree Canopy in Riparian Forest of Maroon River

نویسندگان [English]

  • Ehsan Sayad 1
  • Shaieste Gholami 1
  • Mohammad Reza Askarpour 2

1 Razi University, Kermanshah

2 Behbahan Khatam Alanbia University of Technology

چکیده [English]

Introduction: Sustainability and maintenance of riparian vegetation or restoring of degraded sites is critical to sustain inherent ecosystem function and values. Description of patterns in species assemblages and diversity is an essential step before generating hypotheses in functional ecology. If we want to have information about ecosystem function, soil biodiversity is best considered by focusing on the groups of soil organisms that play major roles in ecosystem functioning when exploring links with provision of ecosystem services. Information about the spatial pattern of soil biodiversity at the regional scale is limited though required, e.g. for understanding regional scale effects of biodiversity on ecosystem processes. The practical consequences of these findings are useful for sustainable management of soils and in monitoring soil quality. Soil macrofauna play significant, but largely ignored roles in the delivery of ecosystem services by soils at plot and landscape scales. One main reason responsible for the absence of information about biodiversity at regional scale is the lack of adequate methods for sampling and analyzing data at this dimension. An adequate approach for the analysis of spatial patterns is a transect study in which samples are taken in a certain order and with a certain distance between samples. Geostatistics provide descriptive tools such as variogram to characterize the spatial pattern of continuous and categorical soil attributes. This method allows assessment of consistency of spatial patterns as well as the scale at which they are expressed. This study was conducted to analyze spatial patterns of soil macrofauna in relation to tree canopy in the riparian forest landscape of Maroon.
Materilas and Methods: The study was carried out in the Maroon riparian forest of the southeasternIran (30o 38/- 30 o 39/ N and 50 o 9/- 50 o 10/ E). The climate of the study area is semi-arid. Average yearly rainfall is about 350.04 mm with a mean temperature of 24.5oc. Plant cover, mainly comprises Populus euphratica Olivie and Tamarix arceuthoides Bge and Lycium shawii Roemer & Schultes. Soil macrofauna were sampled using 175 sampling point along parallel transects (perpendicular to the river). The distance between transects was 100m. We considered distance between samples as 50 m. tree canopy were measured in 5* 5 plots. soil macrofauna were extracted from 50 cm×50 cm×10 cm soil monolith by hand-sorting procedure. All soil macrofauna were identified to family level. Evenness (Sheldon index), richness (Menhinich index) and diversity (Shannon H’ index) by using PAST version 1.39, were determined in each sample. Classical statistical parameters, i.e. mean, standard deviation, coefficient of variation, minimum and maximum, were calculated using SPSS17 software. For analysis of the relationship between Soil macrofauna diversity indices and tree canopy (Total canopy, Populous canopy, Tamarix canopy and Serim canopy) we calculated the correlation among soil properties and macrofauna using the Pearson correlation coefficient. Next, to determining the spatial structure, we calculated the semivariances. Semivariance quantifies the spatial dependence of spatially ordered variable values. In order to gather information about the spatial connection between any two variables, and to compare the similarity of their spatial structure patterns, cross-variograms were constructed. Cross-variograms are plots of cross-semivariance against the lag distance.
Results and Discussion: Soil macrofauna communities were dominated by earthworm, diplopods, coleoptera, gastropoda, araneae, and insect larvae. Correlation analysis of soil macrofauna and tree canopy indicated weak relationships between them. Weak, but significant relationships were found between macrofauna diversity, evenness, richness and total canopy, Populous canopy and Tamarix canopy (positive). Macrofauna indices and tree canopy(excepted Tamarix canopy) were spatially structured; the variograms revealed the presence of spatial autocorrelation. The variograms of variables especially tree canopy, were characterized by relatively large nugget values, which can be explained by sampling error, short range variability, random and inherent variability.Soil macrofauna diversity indices and tree canopy were moderately spatially dependent. Spatial similarity between variables, indicating potential relationships between macrofauna and tree canopy, was evaluated by cross-variograms for pairs of macrofauna indices and measured tree canopy. According to the cross-variograms, using RSS as criterion for model performance, macrofauna diversity were spatially closely related to total tree canopy, Populus canopy. Spatial distribution of soil macrofauna may be influenced by factors like gradients in soil properties and vegetation cover structure. These factors together with intrinsic population processes constitute proximate controlling factors of population structure.
Conclusion: The relationship between macrofauna indices and tree canopy was further explored by means of spatial analyses. Macrofauna indices and tree canopy (excepted Tamarix canopy) were spatially structured. Tree canopy distribution is important for the spatial variability and structure of Soil macrofauna diversity.

کلیدواژه‌ها [English]

  • Cross semivariogram
  • Semivariogram
  • Soil macrofauna
  • Spatial relationship
1- Afshar H., Salehi M. H., Mohammadi J., and Mehnatkesh A. 2009. Spatial variability of soil properties and irrigated wheat yield in a quantitative suitability map, a case study: Shahr-e-Kian area, Chaharmahal va-Bakhtiari province. Journal of Water and Soil, 23(1): 161- 172. (in Persian with English abstract)
2- Aubert M., Hedde M., Decaens T., Bureau F., Margerie P., and Alard D. 2003. Effects of tree canopy composition on earthworms and other macro-invertebrates in beech forests of upper Normandy (France). Pedobiologia, 47: 904- 912.
3- Ayuke F. O., karanja N. K., Muya E. M., Musombi B. K., Mungatu J. and Nyamasyo G. 2009. Macrofauna a diversity and abundance across different Land use systems in Embu Kenya. Tropical and Sub-tropical Agroecosystems, 2009:371- 384.
4- Barrios E. 2007. Soil biota, ecosystem services and land productivity. Ecological Economics, 24(2): 269- 285.
5- Campana C., Gauvin S., and Ponge J.F. 2002. Influence of ground cover on earthworm communities in an unmanaged beech forest: linear gradient studies. European Journal of Soil Biolog, 38: 213- 224.
6- Coleman D.C., Crossley D.A., and Hendrix P.F. 2004. Fundamentals of Soil Ecology, Elsevier Academic Press, Newyork.
7- Dale M.R.T. 1999. Spatial Pattern Analysis in Plant Ecology. Cambridge University Press, London.
8- Deharveng L. 1996. Soil Collembola diversity, endemism, and reforestation: A case study in the Pyrenees (France). Conservation Biology, 10: 74– 84.
9- Fragoso C., Brown G.G., Parton J.C., Blanchart E., Lavelle P., Pashanasi B., Senapati B., and Kumar T. 1997. Agricultural intensification, soil biodiversity and function agroecosystem in thetropics: the role of earthworms. Applied Soil Ecology, 6: 17- 35.
10- Gaston K. J., and Spice J. I. 1998. Biodiversity: an Introduction. Blackwell Science, MA, USA.
11- Giese L.A., Aust W.M., Trettin C.C., and Kolka R.K. 2000. Spatial and temporal patterns of carbon storage and species richness in three South Carolina coastal plain riparian forests. Ecological Engineering, 15: S157- S17.
12- Gillison A., Jones D., Susilo F., and Bignell D. 2003.Vegetation indicates diversity of soil macroinvertebrates:a case study with termites along a land-use intensification gradient in lowland Sumatra. Organisms Diversity & Evolution, 3: 111– 126.
13- Gholami Sh., Hosseini S.M., Mohammadi J., and Mahini A. S. 2011. Spatial variability of soil macrofauna Biomass and soil properties in riparian forest of Karkhe River. Journal of Water and Soil, 25(2): 248- 257. (in Persian with English abstract)
14- Gholami Sh., Mahini A. S., Hosseini S.M., Mohammadi J., and Sayad E. 2014. Study of the vegetation density and soil macrofauna relationship in riparian forest of Karkhe River in order to determine the buffer zone of the river. Applied Ecology, 7: 13- 25. (in Persian)
15- Goovaerts P. 1999. Geostatistics in soil science: state-of-the-art and perspectives. Geoderma, 89: 1- 45.
16- Gonglanski K.B., savin F.A., Pokarzhevskii A.D., and Filimonova, Z.V. 2005. Spatial distribution of isopods in an oak-beech forest. European Journal of soil Biology, 41: 117- 122.
17- Gonglanski K.B., Gorshkova I.A., Karpov A.I., and Pokarzhevskii A.D. 2008. Do boundaries of soil animal and plant communities coincide? A case study of a Mediterranean forest in Russia. European journal of soil biology, 44: 355– 363.
18- Hasani pak A. 1999. Geostatistics. Tehran University Publication. Tehran. (in Persian)
19- Islam M., Chowdhury N., and Osman K., 2009. Faunal population in some forest soils of Chittagong University Campus. World journal of Agricultural Sciences, 5(2): 259- 253.
20- Jimenez J.J., Rossi J.P., and Lavelle P., 2001. Spatial distribution of earthworm in acid-soil savannas of the eastern plains of Colombia. Applied Soil Ecology, 17: 267- 278.
21- Joschko M., Gebbers R., Barkusky D., Rogasik J., Hohn W., Hierold W., Fox C.A., and Timmer J. 2009. Location-dependency of earthworm response to reduced tillage on sand soil. Soil and Tillage Research, 102: 55-66.
22- Katsaliro L., Deng Sh., Nofziger David L., Gerakis A., and Fuhlendorf S. D. 2010. Spatial structure of microbial biomass and activity in prairie soil ecosystems. European Journal of Soil Biology, 46: 181– 189.
23- Leibhold A.M. and Gurevitch J. 2002. Integrating the statistical analysis of spatial data in ecology. Ecography, 25: 553- 557.
24- Mathieu J., Rossi J.P., Grimaldi M., Mora P., Lavelle P., and Rouland C. 2004. A multi-scale study of soil macrofauna biodiversity in Amazonian pastures. Biology and Fertility of Soil, 40: 300- 305.
25- Mathieu J., Grimaldi M., Jouquet P., Rouland C., Lavelle P., Desjardins T., and Rossi J.P. 2009. Spatial pattern of grasses influence soil macrofauna biodiversity in Amazonian pastures. Soil Biology & Biochemistry, 41: 586- 593.
26- Mohmmadi J. 1999. Study of the spatial variability of soil salinity in ramhormoz area (Khuzestan) using geostatistical theory, 1. Kriging. Journal of Science and Technology of Agriculture and Natural Resources, 2(4): 49- 63. (in Persian)
27- Mesdaghi M. Plane Ecology. 2005. Jahad Daneshgahi of Mashhad Publication, Mashhad. (in Persian)
28- Mohmmadi J. 2006. Pedometrics: Geostatistics. Pelk Publication, Tehran. (in Persian).
29- Mohmmadi J. 2006. Pedometrics: Classical Statistics (Univariate & Multivariate). Pelk Publication, 531 p. (in Persian).
30- Nielson D.R., and Wendroth O. 2003. Spatial and temporal statistics, sampling field soils and their vegetation. Geosciences Publishe.
31- Rossi J.P. Lavelle P., and Tondoh J.E. 1995. Statistical tool for soil biology X.Geostatistical analysis. European Journal of Soil Biology, 31(4): 173- 181.
32- Rossi J.P. 2003. Short-range structures in earthworm spatial distribution. Pedobiologia, 47: 582- 587.
33- Salamon J-F., Wissuwa J., Jagos S., and Koblmuller M. 2011. Plant species effects on soil macrofauna density in grassy arable fallows of different age. European Journal of Soil Biology, 47: 129- 137.
34- Sarlo M. 2006. Individual Tree Species Effects on Earthworm Biomass in a Tropical Plantation in Panama. Caribbean Journal of Science, 42: 419- 427.
35- Sun B., Zhou S., and Zhao Q. 2003. Evaluation of spatial and temporal changes of soil quality based on geostatistical analysis in the hill region of subtropical China. Geoderma, 115: 85- 99.
36- Verry E.S., Hornbeck J.W., and Dolloff C.A. 2000. Riparian management in forests of the continental eastern United States. Lewis Publisher.
37- Wardle D. A. 2002. Communities and ecosystems: linking the aboveground and belowground components. Princeton University Press.
CAPTCHA Image