پیش‌بینی و تعیین پارامترهای موثر بر پراکنش حلزون‌های خاکزی با استفاده از مدل-های خطی و غیر خطی در اکوسیستم جنگلی

حلزون های خاکزی، بخش مهمی از اکوسیستم جنگل را شامل می شوند و نقش مهمی در تجزیه لاش‌برگ و غلظت کلسیم خاک دارند. این مطالعه با هدف بررسی ویژگی های خاک و پارامترهای توپوگرافی موثر بر فراوانی حلزون های خاکزی و هم‌چنین پیش بینی پراکنش فراوانی آن‌ها دربخشی از اراضی جنگلی استان گلستان آن‌جام گردید. تعداد 153 نمونه خاک از عمق 010 سانتی متر جمع آوری شد؛  سپس حلزون های خاکزی جمع آوری و تاسطح رده شناسایی و طبقه‌بندی شدند. ویژگی های خاک از طریق آنالیزهای آزمایشگاهی و پارامترهای توپوگرافی، با استفاده از نقشه رقومی ارتفاع منطقه و تصاویر ماهواره ای به‌دست آمدند. بر طبق نتایج حاصل مدل غیر خطی جنگل تصادفی دارای ضریب تبیین 0.49 و خطا 1.82 است و دقت بالاتری نسبت به مدل رگرسیون خطی با ضریب تبیین 0.28 و خطا 2.13 در پیش بینی فراوانی حلزون ها دارد. نتایج به‌دست آمده از آنالیز مولفه های اصلی و آنالیز حساسیت نشان دادند که کربنات کلسیم معادل، ph، ec و کربن آلی، از جمله مهم‌ترین ویژگی های خاکی موثر بر فراوانی حلزون ها هستند. پارامترهای توپوگرافی دارای روابط خطی با فراوانی حلزون های خاکزی نداشته اند اما در مدل غیر خطی به خوبی نقش آن‌ها نشان داده شده است. جهت شیب، ارتفاع و دمای سطح زمین از جمله مهم‌ترین پارامترهای تاثیرگذار بر فراوانی حلزون‌ها بوده اند که احتمالا تاثیر آن‌ها به دلیل تاثیر بر ویژگی های خاک مانند کربنات کلسیم و رطوبت خاک بوده است.

Prediction and determination of effective parameters on the abundance of soil snails using linear and nonlinear models in forest ecosystem

Introduction Soil snails constitute an important part of the forest ecosystem and play an essential role in litter decomposition and soil calcium concentration. Snails are known as bioindicators because of narrow distribution, short lifetime, and high sensitivity (22, 24). The abundance and distribution of soil snails are dependent on different environmental conditions, such as precipitation, pH, soil calcium, and plant cover. Also, soil properties are mainly related to topographic parameters. Because ecosystem components have complex relationships, we need powerful models to find effective factors and spatial variations of the soil fauna (23). Linear Regression and random forest are popular and applicable models in soil science. Up to the present, no study has investigated the effect of soil parameters on snail abundance using linear regression and random forest. This study was performed to investigate the effect of soil properties and topographic parameters on the abundance of soil snails and their distribution in a part of forest area located in Bahramnia forest, an experimental site in Golestan Province, in the north of Iran. Materials and Methods This study was conducted in Shast Kalate (Bahramnia) forest, an experimental forest of Gorgan University of Agricultural Sciences and Natural Resources, located at the eastern Caspian region, north of Iran (36° 43′ 27″ N latitudes, 54°24′ 57″ E longitudes). 153 soil samples were collected from 010 cm; then soil snails were gathered and classified into the Gastropoda taxonomic class group. Soil properties, such as Soil particle size distribution (clay, silt, and sand), soil pH, electrical conductivity (EC), calcium carbonate equivalent (CCE), soil organic carbon (OC), total nitrogen (TN), and Soil microbial respiration (Resp), were measured via laboratory analysis. Also, digital elevation model and satellite images were used to determine the topographic parameters, such as Elevation, slope, slope aspect (Aspect), land surface temperature (land temp) wetness index (WI) and normalized difference vegetation index (NDVI). We used linear regression and nonlinear random forest models for investigating linear and nonlinear relationships between soil properties, topographic parameters, and the abundance of soil snails. Likewise, sensitive analysis was done to find the importance of the input parameters. Results and Discussion The PCA analysis showed that first and second components explain 38 and 21 percent of the variation. In the first component, EC, OC, TN, pH, and silt were the most variable, and in the second component CCE, Clay, OC, sand, and EC were the most important parameters. In both components, topographic parameters had no effect. The PCA graph showed that CCE, sand, and pH had the most correlation with snail abundance and EC, Resp, OC, and TN affected their abundance. The validation results of regression and random forest models showed that random forests have more accuracy (0.49) and low error (1.82). In addition, the sensitive analysis showed that CCE, pH, EC, OC, aspects, elevation, and land temp are the most important parameters on snail abundance. Different studies reported that pH and CCE are effective parameters on snail abundance (20, 17). Also, Ondina., et al. (27) reported that EC has an important effect on soil snail abundance. We hypothesize that topographic parameters affect soil snail nonlinearly and by affecting  soil properties. Aspect is one of the topographic parameters that, via an effect on land temperature, land cover, and pH (8), has an important role in soil snails. In this way, elevation, by affecting  pH, wetness, land temperature, OC, and TN, affects soil snail abundance (13). Land temperature is the other topographic parameter that is affected by aspect and elevation and had a significant effect on snail abundance by affecting  OC and wetness (17). Conclusion Based on the results, nonlinear random forest model had more accuracy than linear regression in predicting snail abundance. Results showed that calcium carbonate equivalent, pH, EC, and organic carbon were the most effective soil priorities on snail abundance. There was no linear relation between soil properties and soil snails, but in the nonlinear model, we found their role. Aspect, elevation, and land temperature were the most effective parameters on snail abundance that probably affected soil properties, such as calcium carbonate and soil moisture.