Stoichiometric distribution models: ecological stoichiometry at the landscape extent

Human activities are altering the fundamental geography of biogeochemicals. Yet we lack an understanding of how the spatial patterns in organismal stoichiometry affect biogeochemical processes and the tools to predict the impacts of global changes on biogeochemical processes. In this contribution we develop stoichiometric distribution models (StDMs), which allow us to map spatial structure in resource elemental composition across a landscape and evaluate spatial responses of consumers. We parameterise StDMs for a consumer‐resource (moose‐white birch) system and demonstrate that we can develop predictive models of resource stoichiometry across a landscape and that such models could improve our predictions of consumer space use. With results from our study system application, we argue that explicit consideration of the spatial patterns in organismal elemental composition may uncover emergent individual, population, community and ecosystem properties that are not revealed at the local extents routinely used in ecological stoichiometry. We discuss perspectives for further developments and application of StDMs to advance three emerging frameworks for spatial ecosystem ecology in an era of global change; meta‐ecosystem theory, macroecological stoichiometry and remotely sensed biogeochemistry. Progress on these emerging frameworks will allow for the integration of ecological stoichiometry and individual space use and fitness.     

物种分布模型的发展及评价方法

物种分布模型已被广泛地应用于以保护区规划、气候变化对物种分布的影响等为目的的研究。回顾了已经得到广泛应用的多种物种分布模型,总结了评价模型性能的方法。基于物种分布模型的发展和应用以及性能评价中尚存在的问题,本文认为:在物种分布模型中集成样本选择模块能够避免模型预测过程中的过度拟合及欠拟合,增加变量选择模块可评估和降低变量之间自相关性的影响,增加生物因子以及将物种对环境的适应性机制(及扩散行为特征)和潜在分布模型进行结合,是提高模型预测性能的可行方案;在模型性能的评价方面,采用赤池信息量可对模型的预测性能进行客观评价。相关建议可为物种分布建模提供参考。     

虚拟物种的基本原理及其在物种分布模型评估中的应用

物种分布模型是建立在物种出现或缺失数据的基础上,但可获得的真实分布数据存在着各种各样的缺点(如:物种识别错误、坐标错误、抽样偏差、数据缺失等),影响着物种分布模型的预测性能、稳定性及应用,因此使用物种真实分布数据评估物种分布模型将带来很大的不确定性。为避免这种不确定性,越来越多的研究使用虚拟物种来评价物种分布模型的性能,评估新方法的优劣。虚拟物种是一种建立在真实(或虚拟)地理信息系统下人工生命,是简化和抽象的物种,它通过模拟物种对环境变量的响应关系,评估物种在不同环境变量下的出现概率,人为地给出虚拟的物种分布数据。虚拟物种具有数据容易获得、数据质量可控、避免过度模拟等优势,目前它被广泛用于评估物种特性、抽样偏差、地理信息、出现/缺失标准等对物种分布模型性能的影响。虚拟物种是大尺度研究中不可或缺的重要工具,有利于解决真实数据未能解决的科学问题。常用的构成算法有求和法、求积法和综合法,但这些方法可能存在补偿效应,扩大了物种的分布范围。考虑到虚拟物种的不足,提出了未来虚拟物种可能的发展方向(避免过度脱离真实,完善虚拟物种的构成算法,构建虚拟的模式生物、群落及生态系统等)。为帮助研究者快速构建虚拟物种,基于R环境开发了一个虚拟物种构成软件包(SDMvspecies)。虚拟物种可以与真实物种相结合,通过改进模型的构成方法,有利于解决一些真实数据未能解决的问题;虚拟物种的应用也将导致一些新理论的产生,有利于更好地理解生态学原理。     

揭示群落结构及其环境响应的联合物种分布模型的研究进展

物种分布模型通常用于基础生态和应用生态研究,用来确定影响生物分布和物种丰富度的因素,量化物种与非生物条件的关系,预测物种对土地利用和气候变化的反应,并确定潜在的保护区.在传统的物种分布模型中,生物的相互作用很少被纳入,而联合物种分布模型(JSDMs)作为近年提出的一种新的可行方法,可以同时考虑环境因素和生物交互作用,因而成为分析生物群落结构和种间相互作用过程的有力工具.JSDMs以物种分布模型(SDMs)为基础,通常采用广义线性回归模型建立物种对环境变量的多变量响应,以随机效应的形式获取物种间的关联,同时结合隐变量模型(LVMs),并基于Laplace近似和马尔科夫蒙脱卡罗模拟的最大似然估计或贝叶斯方法来估算模型参数.本文对JSDMs的产生及理论基础进行归纳总结,重点介绍了不同类型JSDMs的特点及其在现代生态学中的应用,阐述了JSDMs的应用前景、使用过程中存在的问题及发展方向.随着对环境因素与多物种种间关系研究的深入,JSDMs将是今后物种分布模型研究的重点.     

Assessing the accuracy of species distribution models to predict amphibian species richness patterns

1. Evaluating the distribution of species richness where biodiversity is high but has been insufficiently sampled is not an easy task. Species distribution modelling has become a useful approach for predicting their ranges, based on the relationships between species records and environmental variables. Overlapping predictions of individual distributions could be a useful strategy for obtaining estimates of species richness and composition in a region, but these estimates should be evaluated using a proper validation process, which compares the predicted richness values and composition with accurate data from independent sources. 2. In this study, we propose a simple approach to estimate model performance for several distributional predictions generated simultaneously. This approach is particularly suitable when species distribution modelling techniques that require only presence data are used. 3. The individual distributions for the 370 known amphibian species of Mexico were predicted using maxent to model data on their known presence (66,113 presence-only records). Distributions were subsequently overlapped to obtain a prediction of species richness. Accuracy was assessed by comparing the overall species richness values predicted for the region with observed and predicted values from 118 well-surveyed sites, each with an area of c. 100 km(2), which were identified using species accumulation curves and nonparametric estimators. 4. The derived models revealed a remarkable heterogeneity of species richness across the country, provided information about species composition per site and allowed us to obtain a measure of the spatial distribution of prediction errors. Examining the magnitude and location of model inaccuracies, as well as separately assessing errors of both commission and omission, highlights the inaccuracy of the predictions of species distribution models and the need to provide measures of uncertainty along with the model results. 5. The combination of a species distribution modelling method like maxent and species richness estimators offers a useful tool for identifying when the overall pattern provided by all model predictions might be representing the geographical patterns of species richness and composition, regardless of the particular quality or accuracy of the predictions for each individual species.     

The performance of range maps and species distribution models representing the geographic variation of species richness at different resolutions

Aim The method used to generate hypotheses about species distributions, in addition to spatial scale, may affect the biodiversity patterns that are then observed. We compared the performance of range maps and MaxEnt species distribution models at different spatial resolutions by examining the degree of similarity between predicted species richness and composition against observed values from well‐surveyed cells (WSCs). Location Mexico. Methods We estimated amphibian richness distributions at five spatial resolutions (from 0.083° to 2°) by overlaying 370 individual range maps or MaxEnt predictions, comparing the similarity of the spatial patterns and correlating predicted values with the observed values for WSCs. Additionally, we looked at species composition and assessed commission and omission errors associated with each method. Results MaxEnt predictions reveal greater geographic differences in richness between species rich and species poor regions than the range maps did at the five resolutions assessed. Correlations between species richness values estimated by either of the two procedures and the observed values from the WSCs increased with decreasing resolution. The slopes of the regressions between the predicted and observed values indicate that MaxEnt overpredicts observed species richness at all of the resolutions used, while range maps underpredict them, except at the finest resolution. Prediction errors did not vary significantly between methods at any resolution and tended to decrease with decreasing resolution. The accuracy of both procedures was clearly different when commission and omission errors were examined separately. Main conclusions Despite the congruent increase in the geographic richness patterns obtained from both procedures as resolution decreases, the maps created with these methods cannot be used interchangeably because of notable differences in the species compositions they report.     

Diversity of forest plant species at the community and landscape scales in Switzerland

Abstract

Conservation strategies increasingly refer to indicators derived from large biological data. However, such data are often unique with respect to scale and species groups considered. To compare richness patterns emerging from different inventories, we analysed forest species richness at both the landscape and the community scales in Switzerland. Numbers of forest species were displayed using nationwide distributional species data and referring to three different definitions of forest species. The best regression models on a level of four predictor variables ranged between adj. R ² = 0.50 and 0.66 and revealed environmental heterogeneity/energy, substrate (rocky outcrops) and precipitation as best explanatory variables of forest species richness at the landscape scale. A systematic sample of community data (n = 729; 30 m², 200 m², 500 m²) was examined with respect to nationwide community diversity and plot species richness. More than 50% of all plots were assigned to beech forests (Eu-Fagion, Cephalanthero-Fagion, Luzulo-Fagion and Abieti-Fagion), 14% to Norway spruce forests (Vaccinio-Piceion) and 13% to silver fir forests (Piceo-Abietion). Explanatory variables were derived from averaged indicator values per plot, and from biophysical and disturbance factors. The best models for plot species richness using four predictor variables ranged between adj. R ² = 0.31 and 0.34. Light (averaged L-indicator, tree canopy) and substrate (averaged R-indicator and pH) had the highest explanatory power at all community scales. By contrast, the influence of disturbance variables was very small, as only a small portion of plots were affected by this factor. The effects of disturbances caused by extreme events or by management would reduce the tree canopies and lead to an increase in plant species richness at the community scale. Nevertheless, such community scale processes will not change the species richness at the landscape scale. Instead, the variety of different results derived from different biological data confirms the diversity of aspects to consider. Therefore, conservation strategies should refer to value systems.     

Seasonal distribution and species composition of daytime biting mosquitoes

Adults and immatures of Aedes mosquito populations were collected at temperatures between 40 and 44°C (summer), while larvae were collected at 0°C (winter). Major mosquito activities were observed from February to mid-December at various collection sites that yielded high populations of Aedes spp. from May to September, and high populations of Culex spp. and Anopheles spp. from March to September. In June to July, mosquito activity was suspended because the relative humidity was high (70%); a result of the monsoon rains. In August, with temperature ranging from 38 to 42°C, the populations of Culex , Anopheles and Aedes began to increase (36.8, 32.1 and 26.3%, respectively). Population estimates (through standard prototype Centers for Disease Control (CDC) and Biogents (BG)-sentinel) and species composition of Aedes in forest habitats indicated a rapid increase in the populations of Ae. albopictus (52.3%), Ae. aegypti (19.1%) and Ae. vittatus (28.5%) following the rainy season in July. Areas positive for Ae. albopictus had identical population levels and distribution ranges of Ae. vittatus , however, there were no Ae. aegypti in Ae. albopictus areas from August to September. The population level, seasonal distribution, habitat and areas of adult activity marked by global positioning system (GPS) coordinates are being used for reference and for species composition data of Anopheles spp. (2), Culex spp. (10) and Aedes spp. (5) in addition to associated temperature, relative humidity and physico-chemical factors of larval habitat. Global meteorological changes have caused an expansion of the active period, leading to the mosquito's possibility of being a vector of disease increasing, resulting in the spread of dengue fever.     

Impacts of predictor variables and species models on simulating Tamarix ramosissima distribution in Tarim Basin,northwestern China

Aims Preserving and restoring Tamarix ramosissima is urgently required in the Tarim Basin, Northwest China. Using species distribution models to predict the biogeographical distribution of species is regularly used in conservation and other management activities. However, the uncertainty in the data and models inevitably reduces their prediction power. The major purpose of this study is to assess the impacts of predictor variables and species distribution models on simulating T. ramosissima distribution, to explore the relationships between predictor variables and species distribution models and to model the potential distribution of T. ramosissima in this basin.Methods Three models—the generalized linear model (GLM), classification and regression tree (CART) and Random Forests—were selected and were processed on the BIOMOD platform. The presence/absence data of T. ramosissima in the Tarim Basin, which were calculated from vegetation maps, were used as response variables. Climate, soil and digital elevation model (DEM) data variables were divided into four datasets and then used as predictors. The four datasets were (i) climate variables, (ii) soil, climate and DEM variables, (iii) principal component analysis (PCA)-based climate variables and (iv) PCA-based soil, climate and DEM variables.Important findings The results indicate that predictive variables for species distribution models should be chosen carefully, because too many predictors can reduce the prediction power. The effectiveness of using PCA to reduce the correlation among predictors and enhance the modelling power depends on the chosen predictor variables and models. Our results implied that it is better to reduce the correlating predictors before model processing. The Random Forests model was more precise than the GLM and CART models. The best model for T. ramosissima was the Random Forests model with climate predictors alone. Soil variables considered in this study could not significantly improve the model's prediction accuracy for T. ramosissima. The potential distribution area of T. ramosissima in the Tarim Basin is ~3.57 × 10 4 km 2, which has the potential to mitigate global warming and produce bioenergy through restoring T. ramosissima in the Tarim Basin.     

Occupancy and density of a habitat specialist and a sympatric generalist songbird species in Tasmania

Patterns of distribution and abundance of species are dependent on their particular ecological requirements. Taking specialisation into account is important for interpreting population parameters. Here, we evaluate population parameters of an endangered habitat specialist, the forty‐spotted pardalote (Pardalotus quadragintus; dependent on white gum Eucalyptus viminalis in south‐eastern Tasmania), and a sympatric congeneric habitat generalist, the striated pardalote (Pardalotus striatus). We used occupancy models to estimate occupancy of both species, and distance sampling models to estimate population density and size on North Bruny Island. Within their shared habitat (i.e. white gum forest), we also fitted hierarchical distance sampling models to estimate density in relation to fine‐scale habitat features. We show that forty‐spotted pardalotes only occurred in forests where white gums were present, with a mean density of 2.7 birds per hectare. The density of forty‐spotted pardalotes decreased in areas with abundant small trees and trees with dead crowns, but they increased in areas where larger white gums were abundant. The striated pardalote was widespread, but where white gums were present, they occurred at 2.1 birds per hectare, compared to 0.6 birds per hectare in forests where white gums were absent. Within white gum habitat, the relative abundance of forty‐spotted pardalotes and dead trees had a positive effect on the density of striated pardalotes while small trees had a negative effect. Our study reveals that although widespread, the generalist is most abundant in the limited areas of habitat suitable for the specialist, and this indicates the need of future research to look at whether this pattern of occurrence exacerbates competition in resource depleted habitats.     

Understanding species distribution in dynamic populations: a new approach using spatio‐temporal point process models

Understanding and predicting a species’ distribution across a landscape is of central importance in ecology, biogeography and conservation biology. However, it presents daunting challenges when populations are highly dynamic (i.e. increasing or decreasing their ranges), particularly for small populations where information about ecology and life history traits is lacking. Currently, many modelling approaches fail to distinguish whether a site is unoccupied because the available habitat is unsuitable or because a species expanding its range has not arrived at the site yet. As a result, habitat that is indeed suitable may appear unsuitable. To overcome some of these limitations, we use a statistical modelling approach based on spatio‐temporal log‐Gaussian Cox processes. These model the spatial distribution of the species across available habitat and how this distribution changes over time, relative to covariates. In addition, the model explicitly accounts for spatio‐temporal dynamics that are unaccounted for by covariates through a spatio‐temporal stochastic process. We illustrate the approach by predicting the distribution of a recently established population of Eurasian cranes Grus grus in England, UK, and estimate the effect of a reintroduction in the range expansion of the population. Our models show that wetland extent and perimeter‐to‐area ratio have a positive and negative effect, respectively, in crane colonisation probability. Moreover, we find that cranes are more likely to colonise areas near already occupied wetlands and that the colonisation process is progressing at a low rate. Finally, the reintroduction of cranes in SW England can be considered a human‐assisted long‐distance dispersal event that has increased the dispersal potential of the species along a longitudinal axis in S England. Spatio‐temporal log‐Gaussian Cox process models offer an excellent opportunity for the study of species where information on life history traits is lacking, since these are represented through the spatio‐temporal dynamics reflected in the model.     

Using species distribution models to identify suitable areas for biofuel feedstock production

The 2007 Energy Independence and Security Act mandates a five‐fold increase in US biofuel production by 2022. Given this ambitious policy target, there is a need for spatially explicit estimates of landscape suitability for growing biofuel feedstocks. We developed a suitability modeling approach for two major US biofuel crops, corn (Zea mays) and switchgrass (Panicum virgatum), based upon the use of two presence‐only species distribution models (SDMs): maximum entropy (Maxent) and support vector machines (SVM). SDMs are commonly used for modeling animal and plant distributions in natural environments, but have rarely been used to develop landscape models for cultivated crops. AUC, Kappa, and correlation measures derived from test data indicate that SVM slightly outperformed Maxent in modeling US corn production, although both models produced significantly accurate results. When compared with results from a mechanistic switchgrass model recently developed by Oak Ridge National Laboratory (ORNL), SVM results showed higher correlation than Maxent results with models fit using county‐scale point inputs of switchgrass production derived from expert opinion estimates. However, Maxent results for an alternative switchgrass model developed with point inputs from research trial sites showed higher correlation to the ORNL model than the corresponding results obtained from SVM. Further analysis indicates that both modeling approaches were effective in predicting county‐scale increases in corn production from 2006 to 2007, a time period in which US corn production increased by 24%. We conclude that presence‐only methods are a powerful first‐cut tool for estimating relative land suitability across geographic regions in which candidate biofuel feedstocks can be grown, and may also provide important insight into potential land‐use change patterns likely to be associated with increased biofuel demand.