The effect of exposure on landscape scale soil surface temperatures and species distribution models

Species distribution models (SDMs) often use elevation as a surrogate for temperature or utilise elevation sensitive interpolations from weather stations. These methods may be unsuitable at the landscape scale, especially where there are sparse weather stations, dramatic variations in exposure or low elevational ranges. The goal of this study was to determine whether radiation, moisture or a novel estimate of exposure could improve temperature estimates and SDMs for vegetation on the Illawarra Escarpment, near Sydney, Australia. Forty temperature sensors were placed on the soil surface of an approximately 12,000 ha study site between November 2004 and August 2006. Linear regression was used to determine the relationship with environmental factors. Elevation was correlated more with moderate temperatures (winter maximums, summer minimums, spring and autumn averages) than extreme temperatures (summer maximums, winter minimums). The correlation (r ²) between temperature and environmental factors was improved by up to 0.38 by incorporating exposure, moisture and radiation in the regressions. Summer maximums and winter minimums were predominately determined by exposure to the NW and coastal influences respectively, while exposure to the NE and SW was important during other seasons. These directions correspond with the winds that are most influential in the study area. The improved temperature estimates were used in Generalised Additive Models for 37 plant species. The deviance explained by most models was increased relative to elevation, especially for moist rainforest species. It was concluded that improving the accuracy of seasonal temperature estimates could improve our ability to explain the patchy distribution of many species. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

黄海南部和东海中北部黄鮟鱇数量分布及其与环境的关系

根据2004年4、6、9月和12月在黄海南部和东海中北部(27°00′~34°00′N、122°00′~127°00′E)进行的渔业资源大面定点底拖网调查资料,以相对资源密度指数作为指标值,分析了黄鮟鱇的时空分布特征。并利用GAM模型,分析了黄鮟鱇数量分布与经纬度、水深、底温和底盐等环境因子的关系。结果表明:黄鮟鱇主要分布在黄海南部和东海北部海区,4个季节的分布范围有明显不同,以春季为最大,冬季最小;黄鮟鱇适宜底温、底盐和水深范围分别为11~14℃、33.5~34.5和40~90 m;根据黄鮟鱇分布的底温范围,推断黄鮟鱇为冷温性鱼种。     

南太平洋长鳍金枪鱼延绳钓渔获率与环境因子的关系研究

为掌握不同水层的环境因子对长鳍金枪鱼(Thunnus alalunga)延绳钓渔获率的影响,根据2015-2017年中国大陆在该海域的长鳍金枪鱼延绳钓渔捞日志资料,结合同期海洋环境数据,采用广义可加模型(Generalized additive model,GAM)对渔获率与各因子的关系进行研究。通过相关分析获取各环境因子相关系数,对相关性较大的环境因子分组建模。结果表明:1)海表面温度与120 m水深温度、海表面温度与海表面高度、120 m水深温度与海表面高度、300 m水深温度与300 m水深盐度为高度相关因子,海表面盐度、叶绿素a浓度、海表风场南北分量与其他环境因子之间的相关性均较小;2)模型的总解释偏差介于30%~40%,各环境因子重要性依次为120 m水深温度、海表温度、300 m水深温度、120 m水深盐度、海表面高度、300 m水深盐度、海表盐度、混合层深度、海面风场南北分量、海面风场东西分量、叶绿素a浓度;3)120 m水深温度与单位捕捞努力渔获量(CPUE)在15~30℃呈负相关。海表温度整体趋势与120 m水深温度类似,其中在25~28℃呈正相关。300 m水深温度与CPUE在10~18℃呈现明显的正效应关系。     

基于GAM模型的马鞍列岛海域优势甲壳类与环境因子的关系研究

海洋虾类和虾蛄等甲壳类是许多鱼类的饵料生物,是海洋生态系统的重要组成部分。为了探索马鞍列岛海域优势甲壳类与环境因子之间的关系,文章基于广义加性模型(GAM)建立了甲壳类与环境因子的生态模型,并对甲壳类资源分布进行预测。结果表明,影响细巧仿对虾(Parapenaeopisis tenella)分布的主要因素是时间因素和底质,影响口虾蛄(Squilla orarotia)分布的主要因素包括底质、经度、温度和盐度,而影响葛氏长臂虾(Palaemon gravieri)分布的显著因素是时间因素。从分布格局来看,细巧仿对虾生物量低值出现在研究海域中间区域,高值出现在东北部海域;3月口虾蛄生物量在东南海域较高,而10月在西部海域较高;葛氏长臂虾分布低值出现在研究海域中间区域,高值海域环绕低值海域周围分布。     

北太平洋公海日本鲭资源分布及其渔场环境特征

根据2014~2015年两年收集的北太平洋公海围拖网作业的日本鲭(Scomber japonicas,又称鲐鱼)生产月度数据,结合同期卫星遥感反演技术获取的海表温度(SST)、海水叶绿素a(Chl-a)浓度、海流等环境数据,运用渔获量重心法,地统计插值等方法,分析了北太平洋公海鲐鱼的资源分布情况与渔获量重心的时空变化及其与主要环境因子之间的关系。研究表明,鲐鱼渔场季节性差异明显,渔场重心集中分布在39°N~43°N、147°E~154°E范围内。两年渔场重心均呈现先向东北方向移动,自9月开始再向西南方向移动的趋势。GAM模型显示,北太平洋鲐鱼渔场的最适海表温度范围是16~18℃,最适叶绿素a浓度范围是0.3~0.8 mg·m~(-3),空间上集中分布在40°N~41°N、148°E~151°E,海流对鲐鱼渔场形成尤为重要。     

Gas diffusivity in soils compared to ideal isotropic porous media

Evidence of anisotropy is reported for advective air and water permeabilities in soils. Thus, anisotropy is likely to exist also for diffusive gas fluxes. Information about direction‐dependent soil gas diffusivity is scarce and most modeling approaches assume isotropy. At hundreds of closely lying positions in a compacted and adjacent undisturbed forest soil, gas diffusivity (D_s/D₀) was measured either in vertical or horizontal direction. The volume‐independent diffusion efficiency (i.e., diffusivity divided by air‐filled porosity) was fitted by a generalized additive model (GAM). Significant regressors were air‐filled porosity (?), soil depth, and the discrete diffusion direction. The model yields in all cases higher vertical diffusion efficiencies. The compaction factor did not yield a significant regressor of its own, i.e., the reduction of diffusivity in the compacted soil was the same as in low‐porosity samples of the undisturbed profile. To elucidate the role of sharing vertically and horizontally orientated pore space and a potential competition between diffusivity in different spatial directions, simple geometric models consisting of 3‐dimensionally crossed pores have been parameterized. These models provided a good explanation of the typical nonlinear D_s/D₀(?) relationship. By simple one‐parameter correction (linear or power function), this mechanistic model could be fitted to the data. The one‐parameter correction of the geometric model could be a straightforward approach to consider direction dependence of measured diffusivities. However, by applying this approach to the observations the anisotropy effect was not clearly evident, which could be attributed to a changing D_s/D₀(?) relationship with depth. As a reason for the preference of the vertical gas diffusion the dominance of vertical stresses and the activity of anecic earthworms are discussed. Direction dependency of gas diffusivity seems to be a basic feature of natural pore systems and has to be considered for modeling gas fluxes in soils. Generally, a preferential vertical diffusion direction reduces horizontal balancing and increases the heterogeneity of gas concentrations in the soil air.     

Soil available water capacity interpolation and spatial uncertainty modelling at multiple geographical extents

Assessment of the spatial distribution of environmental variables and of the associated uncertainty is a key issue in environmental modelling. The water content of soil plays an important role in many ecological and hydrological processes for land suitability evaluation. In this study we present a flexible procedure to interpolate soil-related variables that uses covariates to estimate the spatial trend of the variables and quantifies the uncertainty dealing with non-linear relationships. The procedure further extends approaches based on generalized additive models. The use of Gaussian simulations of the error allows the assessment of spatial uncertainty. The method was applied to available soil water capacity for three different nested extents: national, regional, and catchment. The models fitted have different significant covariates and different estimated values according to the region considered. The results suggest that the estimates from the model fitted at the appropriate extent are the most accurate. Taking into account the uncertainty of the trend, the results provided a realistic estimation of the variability and they are spatially consistent with the geomorphological patterns. Estimating the variability with the proposed procedure is useful for further environmental and land use modelling and it can be integrated with uncertainty from other variables, such as those derived from climate models.     

GLM和GAM模型研究东黄海鲐资源渔场与环境因子的关系

鲐是我国近海重要中上层鱼类,研究其资源变动、渔场分布与时空、海洋环境因子之间的关系有利于该资源的合理开发和利用.根据1998-2004年我国东黄海大型鲐围网渔业的生产统计和时间、空间、表温、表层盐度、表温梯度、表温的月差异等环境数据,利用广义可加模型(GAM)和广义线性模型(GLM)对鲐资源丰度和环境因子的关系进行研究.结果表明,在南部海域,作业渔场集中在122.5°E～124°E、26.5°N～28°N,适宜表温26.5～30℃,适宜表层盐度33.3～34.3,并明显集中在锋区周边海域;在北部海域,作业渔场集中在122.5°E～125.5°E、33°N～37.5°N,适宜表温15～20℃,适宜表层盐度31.3～32.3,集中在冷水区边缘海域.相对资源密度指数大于0.5的海域为122°30'E～124°30'E、26°30'N～28°N,122°30'E～125°30'E、33°N～34°30'N和124°E～125°E、34°30'N～37°N.研究认为,南北不同海域鲐分布的适宜表温和表层盐度差异明显.影响鲐资源丰度的环境因子重要性依次为时间、空间和海洋环境.     

Modelling stream fish species distribution in a river network: the relative effects of temperature versus physical factors

Abstract – The relative influence of temperature versus local physical factors on the spatial distribution of riverine fish species was investigated in a large watershed of south-western France. Using generalised additive models and hierarchical partitioning, we modelled the ecological responses of 28 fish species to a set of five environmental predictors, and we quantified the independent effect of each predictor. The spatial distribution of fish species was primarily determined by both mean temperature and position along the upstream–downstream gradient. However, responses to these environmental factors varied according to the species considered. Fish species with strong thermal requirements (e.g., common carp, black bullhead, Atlantic salmon) were mainly sensitive to temperature whereas longitudinal gradient was of primary importance for downstream species (e.g., common bream, largemouth bass, pike perch). Both the statistical methods used gave concordant results and appeared complementary. This dual-approach, quantifying the relative contribution of each environmental factor, appears particularly useful to understand the spatial distribution of stream fish species. Separating the effects of temperature versus habitat factors is crucial to accurately predict species distribution modifications in the current context of global change.     

滇中云南松胸径和树高生长的GAM模型

阐述利用GAM模型对林木的胸径和树高进行模拟的原因，并结合云南松胸径和树高的实测数据，分别用传统的GM（1，1）模型和GAM（1）模型进行模拟．模型精度检验结果，GAM（1）模型具有更高的模拟精度；实际检验分析表明，GAM（1）模型具有重要的应用价值．