This paper describes the weight–length relationships (WLRs) and Fulton’s condition factors (K) of skipjack tuna (Katsuwonus pelamis) in purse seine fisheries from three cruises in the western and central Pacific Ocean (WCPO): August–September 2009 (AS09), November–December 2012 (ND12), and June–July 2013 (JJ13). The fork length and weight of a total of 1678 specimens were measured. The results showed that the fork length of more than 70% of specimens was below 60 cm (76% in AS09, 87% in ND12, and 73% in JJ13). The coefficient b in the combined sex group was 3.367, 3.300 and 3.234 in JJ13, AS09 and ND12, respectively. The b values of WLRs when fork length was >60 cm were significantly less than 3 (P = 0.062), but when fork length was <60 cm they were significantly greater than 3 (P = 0.028). The K value ranges of JJ13, AS09 and ND12 in different fork length groups were 1.3–1.84 (1.62 ± 0.18), 1.57–2.02 (1.86 ± 0.15), and 1.44–1.78 (0.65 ± 0.13), respectively. Moreover, K values in different fork length classes for each cruise had one turning point: 60–65 cm for JJ13; 60–65 cm for ND12; and 55–60 cm for AS09. The results of this study provide basic information on the WLRs and K values of skipjack tuna in different seasons and growth phases in the WCPO, which are useful for fishery biologists and fishery managers.

The explicit mapping of spatial soil pH is beneficial to evaluate the effects of land-use changes in soil quality. Digital soil mapping methods based on machine learning have been considered one effective way to predict the spatial distribution of soil parameters. However, selecting optimal environmental variables with an appropriate feature selection method is key work in digital mapping. In this study, we evaluated the performance of the support vector machine recursive feature elimination (SVM-RFE) feature selection methods with four common performance machine learning methods in predicting and mapping the spatial soil pH of one urban area in Fuzhou, China. Thirty environmental variables were collected from the 134 samples that covered the entire study area for the SVM-RFE feature selection. The results identified the five most critical environmental variables for soil pH value: mean annual temperature (MAT), slope, Topographic Wetness Index (TWI), modified soil-adjusted vegetation index (MSAVI), and Band5. Further, the SVM-RFE feature selection algorithm could effectively improve the model accuracy, and the extreme gradient boosting (XGBoost) model after SVM-RFE feature selection had the best prediction results (R² = 0.68, MAE = 0.16, RMSE = 0.26). This paper combines the RFE-SVM feature selection with machine learning models to enable the fast and inexpensive mapping of soil pH, providing new ideas for predicting soil pH at small and medium scales, which will help with soil conservation and management in the region.

This paper describes the distribution of living coccolithophores (LCs) in the Yellow Sea and the East China Sea in summer and winter, and its relationship with environmental factors by canonical correspondence analysis (CCA). We carried out a series of investigations on LCs distribution in the Yellow Sea and the East China Sea in July and December 2011. 210 samples from different depths were collected from 44 stations in summer and 217 samples were collected from 45 stations in winter. Totally 20 taxa belonging to coccolithophyceae were identified using a polarized microscope at the 1000 × magnification. The dominant species of the two seasons were Gephyrocapsa oceanica, Emiliania huxleyi, Helicosphaera carteri, and Algirosphaera robusta. In summer the abundance of coccolithophore cells and coccoliths ranged 0–176.40 cells mL⁻¹, and 0–2144.98 coccoliths mL⁻¹, with the average values of 8.45 cells mL⁻¹, and 265.42 coccoliths mL⁻¹, respectively. And in winter the abundance of cells and coccoliths ranged 0–71.66 cells mL⁻¹, and 0–4698.99 coccoliths mL⁻¹, with the average values of 13.91 cells mL⁻¹ and 872.56 coccoliths mL⁻¹, respectively. In summer, the LCs in surface layer were mainly observed on the coastal belt and southern part of the survey area. In winter, the LCs in surface layer had high value in the continental shelf area of section P. The comparison among section A, section F, section P and section E indicated lower species diversity and less abundance in the Yellow Sea than those in the East China Sea in both seasons. Temperature and the nitrate concentration may be the major environmental factors controlling the distribution and species composition of LCs in the studying area based on CCA. Abbreviations: LCs: Living Coccolithophores; CCA: canonical correspondence analysis; DCM: Deep Chlorophyll Maximum

Soil C and N turnover rates and contents are strongly influenced by climates (e.g., mean annual temperature MAT, and mean annual precipitation MAP) as well as human activities. However, the effects of converting natural forests to intensively human-managed plantations on soil carbon (C), nitrogen (N) dynamics across various climatic zones are not well known. In this study, we evaluated C, N pool and natural abundances ofδ¹³C andδ¹⁵N in forest floor layer and 1-meter depth mineral soils under natural forests (NF) and plantation forest (PF) at six sites in eastern China. Our results showed that forest floor had higher C contents and lower N contents in PF compared to NF, resulting in high forest floor C/N ratios and a decrease in the quality of organic materials in forest floor under plantations. In general, soil C, N contents and their isotope changed significantly in the forest floor and mineral soil after land use change (LUC). Soilδ¹³C was significantly enriched in forest floor after LUC while bothδ¹³C andδ¹⁵N values were enriched in mineral soils. Linear and non-linear regressions were observed for MAP and MAT in soil C/N ratios and soilδ¹³C, in their changes with NF conversion to PF while soilδ¹⁵N values were positively correlated with MAT. Our findings implied that LUC alters soil C turnover and contents and MAP drive soilδ¹³C dynamic.