Realizing the potential of trait‐based approaches to advance fisheries science

Analysing how fish populations and their ecological communities respond to perturbations such as fishing and environmental variation is crucial to fisheries science. Researchers often predict fish population dynamics using species‐level life‐history parameters that are treated as fixed over time, while ignoring the impact of intraspecific variation on ecosystem dynamics. However, there is increasing recognition of the need to include processes operating at ecosystem levels (changes in drivers of productivity) while also accounting for variation over space, time and among individuals. To address similar challenges, community ecologists studying plants, insects and other taxa increasingly measure phenotypic characteristics of individual animals that affect fitness or ecological function (termed “functional traits”). Here, we review the history of trait‐based methods in fish and other taxa, and argue that fisheries science could see benefits by integrating trait‐based approaches within existing fisheries analyses. We argue that measuring and modelling functional traits can improve estimates of population and community dynamics, and rapidly detect responses to fishing and environmental drivers. We support this claim using three concrete examples: how trait‐based approaches could account for time‐varying parameters in population models; improve fisheries management and harvest control rules; and inform size‐based models of marine communities. We then present a step‐by‐step primer for how trait‐based methods could be adapted to complement existing models and analyses in fisheries science. Finally, we call for the creation and expansion of publicly available trait databases to facilitate adapting trait‐based methods in fisheries science, to complement existing public databases of life‐history parameters for marine organisms.     

Evaluation of Intestinal Absorption of Dietary Halocynthiaxanthin,a Carotenoid from the Sea Squirt Halocynthia roretzi

Halocynthiaxanthin is an acetylenic carotenoid mainly found in Halocynthia roretzi. To date, several bioactivities of halocynthiaxanthin have been reported, but its mechanism of digestion and absorption in mammals has not been studied yet. In this study, we evaluated the intestinal absorption of halocynthiaxanthin in mice. The halocynthiaxanthin-rich fraction was prepared from the tunicate Halocynthia roretzi. Mice were orally administered the fraction at a dose of 5 mg/kg body weight. The halocynthiaxanthin levels in the plasma, liver, and small intestine, were quantified using HPLC-PDA, 1, 3, 6, and 9 h after ingestion. The halocynthiaxanthin-rich fraction mainly consisted of the all-trans form and a small amount of cis forms. These three isomers were detected in the plasma of mice 3 h after ingestion. Time-course changes after the ingestion of this fraction were found, with cis isomers being more abundant than the all-trans isomer in the mouse plasma and liver. In the small intestine, however, the all-trans isomer was primarily detected. The possibility that cis isomers might be absorbed rapidly from the small intestine cannot be denied, but our results suggest that dietary all-trans-halocynthiaxanthin might be isomerized to the cis isomer after intestinal absorption.     

Allometric Estimates of Aboveground Biomass Using Cover and Height Are Improved by Increasing Specificity of Plant Functional Groups in Eastern Australian Rangelands

Plant aboveground biomass (AGB) is a useful metric to assess ecosystem functioning, and its sensitivity to changing environmental conditions provides insight into potential global change impacts. Allometric estimates of AGB using vegetation characteristics such as plant cover or height provide nondestructive biomass proxies for repeated measurements but can introduce uncertainty to estimates. We estimated the relationship between both plant cover and a cover·height index and AGB for 15 plant species from six sites to identify the most reliable approach to estimate biomass nondestructively in semiarid eastern Australian rangelands. Estimates were made by grouping species at four different levels of specificity, to test whether generic estimates were more robust than grouping species based on life history and morphological characteristics. Estimates were then tested on a 1.5-m² plot at each site for validation. In all cases, models were highly significant (P < 0.001) with adjusted R² values ranging from 0.42 to 0.96 for cover models and 0.38 to 0.98 for cover·height index models. We found the addition of height improved model fits in four groups while reducing model fits in two groups. The error around AGB estimates for cover·height index−based models ranged from −66.8 to 4% (absolute mean 35%). Cover-based models had errors between −13.4% and 53% (absolute mean 14.2%). For cover-based estimates of AGB in validation plots, grouping plants by plant functional types (PFTs) increased accuracy (absolute mean error 17.3%) compared with estimates using data from all 15 species (absolute mean of 65.2%). Overall cover was a useful surrogate to estimate AGB (with the exception of one site, accuracy ranged from −2.3% to 11.5%), while height (thought to be a surrogate for canopy characteristics) provided benefit in a few circumstances. We suggest that future research should test additional nondestructive proxies and group species based on PFTs to improve AGB estimates using allometry.     

基于SegNet模型的高原鼠兔的图像分割

针对高原鼠兔图像目标尺寸小、背景复杂、特征不显著、基于活动轮廓的图像分割模型无法有效分割的问题，采用基于卷积神经网络的SegNet语义模型对高原鼠兔图像进行分割：首先将采集的高原鼠兔图像进行预处理，尺度归一化后制作成与Pascal VOC数据集格式一致的数据集；然后将数据集分为训练集与测试集，采用训练集对SegNet模型训练，测试集对模型进行分割测试。对高原鼠兔图像分割的试验结果表明：与基于活动轮廓的Chan_Vese模型相比，基于卷积神经网络的SegNet模型对高原鼠兔图像分割时的交并比、平均像素精度、Dice相似性指数和Jaccard指数分别提高了68.33%、9.35%、30.61%和47.98%，过分割率和欠分割率分别降低了87.20%、16.52%。     

基于PLSR-BP复合模型的红壤有机质含量反演研究

对红壤地区土壤有机质进行快速预测,以满足智慧农业与精准施肥的需要。以江西省奉新县北部为研究区域,采用1 km×1 km标准格网划分研究区进行采样,共得到红壤样本248个。对土壤光谱进行了包含分数阶导数在内的3种数学变换方法,将经过P=0.01显著性检验的波段用于模型的构建,选用偏最小二乘回归(PLSR)和BP神经网络建立土壤有机质含量预测模型。结果表明:当对红壤光谱数据进行1.5阶导数变换后再使用PLSR-BP复合模型对土壤有机质含量进行预测时的结果为最优,训练集R~2=0.89,RMSE=4.68g·kg~(-1),验证集R~2=0.87,RMSE=5.55g·kg~(-1),RPD=2.75。1.5阶导数对红壤光谱数据的变换能够更好地突出与有机质相关的特征信息,有助于其含量预测。PLSR-BP复合模型预测精度优于单一模型,能够较好地预测红壤有机质含量,为精准农业快速监测红壤有机质含量提供了新的途径。     

大连市中心区声环境功能区划调整研究

在分析了大连市中心区声环境功能区划调整必要性的基础上,全面评估了现有区划实施效果,以问题为导向提出了区划调整的方法体系,并阐述了区划调整结果的可行性,为城市环境噪声管理和防治提供科学依据。     

三种酶制备牡蛎蛋白水解物的抗氧化活性和功能特性分析

目的：比较分析木瓜蛋白酶、胃蛋白酶和胰蛋白酶三种不同酶水解牡蛎获得的蛋白产物抗氧化活性和功能特性。方法：采用木瓜蛋白酶、胃蛋白酶和胰蛋白酶三种不同酶水解牡蛎蛋白获得水解物,并分别测定它们的在不同pH下的溶解度、乳化性以及不同浓度水解物的还原能力、DPPH自由基清除率和Fe^{2 +}螯合能力。结果：表明三种酶水解物都有较高的溶解度(p＜0.01),胰蛋白酶产生的水解物溶解高度达94.8％(p＜0.05)；水解物溶解度和乳化活性指数在pH4显著降低(p＜0.05)。牡蛎蛋白质水解产物的还原能力、DPPH自由基清除能力和金属螯合活性随浓度的增加而增强,其中胰蛋白酶和胃蛋白酶抗氧化能力较强(p＜0.01)。结论：三种蛋白酶水解牡蛎蛋白可获得抗氧化活性强、溶解度高的水解物,并受浓度和pH的影响,它们的水解物可作为一种潜在的抗氧化功能多肽利用。     

干制方法对枇杷叶总酚、黄酮等成分及抗氧化活性的影响

研究不同干制方法对枇杷叶总酚、黄酮等成分和抗氧化活性的影响，给生产实践提供技术依据和理论参考，以成熟枇杷叶作为试验材料，采用热风烘干（35℃、50℃、65℃、80℃）、自然晒干和阴干方式进行干制处理，用70%乙醇超声浸提，测定其黄酮、总酚、多糖含量及抗氧化活性。结果表明，不同干制条件下枇杷叶总酚、黄酮、多糖含量及抗氧化活性存在差异，其中阴干条件下所测成分含量及抗氧化活性最高，热风烘干条件下随着温度的升高，试验所测成分含量及抗氧化活性呈下降趋势。阴干可以较大程度地保留枇杷叶总酚、黄酮等成分及其抗氧化活性。在以干燥枇杷叶为原料，以总酚、黄酮等成分和抗氧化性为主的生产利用方面，应以阴干方法进行干制处理。