Incorporating uncertainty in predictive species distribution modelling

Motivated by the need to solve ecological problems (climate change, habitat fragmentation and biological invasions), there has been increasing interest in species distribution models (SDMs). Predictions from these models inform conservation policy, invasive species management and disease-control measures. However, predictions are subject to uncertainty, the degree and source of which is often unrecognized. Here, we review the SDM literature in the context of uncertainty, focusing on three main classes of SDM: niche-based models, demographic models and process-based models. We identify sources of uncertainty for each class and discuss how uncertainty can be minimized or included in the modelling process to give realistic measures of confidence around predictions. Because this has typically not been performed, we conclude that uncertainty in SDMs has often been underestimated and a false precision assigned to predictions of geographical distribution. We identify areas where development of new statistical tools will improve predictions from distribution models, notably the development of hierarchical models that link different types of distribution model and their attendant uncertainties across spatial scales. Finally, we discuss the need to develop more defensible methods for assessing predictive performance, quantifying model goodness-of-fit and for assessing the significance of model covariates.     

白花油麻藤的地理分布及适生区预测

白花油麻藤是豆科黧豆属大型木质藤本植物。运用Diva-Gis软件,结合海拔高度图层和植被图层绘制了白花油麻藤的地理分布图,分析了白花油麻藤的分布规律和生境特性;以Maxent模型作为物种适生性预测模型,温度和降水作为预测的环境因子,预测了白花油麻藤在中国的适生区。结果表明:白花油麻藤在中国27.5°N以南中低海拔10～1200m有分布,如山坡、路旁、沟谷、溪边及林下灌丛;喜温暖湿润气候,广东为白花油麻藤分布最为密集的地区。白花油麻藤的分布与植被类型和海拔有着密切的关系,分布区的植被类型为亚热带常绿阔叶林和热带季雨林,随着植被分布密度的降低和海拔的升高白花油麻藤的分布范围和分布密度呈逐渐缩小的趋势。白花油麻藤在中国的潜在分布区为粤、桂、闽、港、澳、滇、琼、赣、川、黔、藏、湘、浙等省区及交界处,其种质资源的保存及其利用应考虑其潜在分布区。     

Estimating incident population distribution from prevalent data

A prevalent sample consists of individuals who have experienced disease incidence but not failure event at the sampling time. We discuss methods for estimating the distribution function of a random vector defined at baseline for an incident disease population when data are collected by prevalent sampling. Prevalent sampling design is often more focused and economical than incident study design for studying the survival distribution of a diseased population, but prevalent samples are biased by design. Subjects with longer survival time are more likely to be included in a prevalent cohort, and other baseline variables of interests that are correlated with survival time are also subject to sampling bias induced by the prevalent sampling scheme. Without recognition of the bias, applying empirical distribution function to estimate the population distribution of baseline variables can lead to serious bias. In this article, nonparametric and semiparametric methods are developed for distribution estimation of baseline variables using prevalent data.     

三种植被冠层二向反射分布函数模型的比较

随着定量遥感技术的发展, 描述森林冠层二向反射分布函数(BRDF)的机理模型越来越多。该研究采用3种植被冠层BRDF模型——DART模型、4SCALE模型和MGEOSAIL模型, 模拟了不同郁闭度样地在红光、近红外波段各个观测角度下的场景反射率, 并比较分析了不同BRDF模型的适用性和局限性。结果表明: MGEOSAIL模型只适于模拟样地郁闭度较小、林木个体较大条件下的场景反射率, 且热点效果不十分明显; DART模型和4SCALE模型适于任何郁闭度条件下的场景反射率的模拟, 并且精度较高; 4SCALE模型模拟的场景反射率介于DART模型模拟的1次散射与5次散射之间。这3种模型在模拟近红外波段的场景反射率时, 均存在“碗边”效应。     

A standard protocol for reporting species distribution models

Species distribution models (SDMs) constitute the most common class of models across ecology, evolution and conservation. The advent of ready-to-use software packages and increasing availability of digital geoinformation have considerably assisted the application of SDMs in the past decade, greatly enabling their broader use for informing conservation and management, and for quantifying impacts from global change. However, models must be fit for purpose, with all important aspects of their development and applications properly considered. Despite the widespread use of SDMs, standardisation and documentation of modelling protocols remain limited, which makes it hard to assess whether development steps are appropriate for end use. To address these issues, we propose a standard protocol for reporting SDMs, with an emphasis on describing how a study's objective is achieved through a series of modeling decisions. We call this the ODMAP (Overview, Data, Model, Assessment and Prediction) protocol, as its components reflect the main steps involved in building SDMs and other empirically-based biodiversity models. The ODMAP protocol serves two main purposes. First, it provides a checklist for authors, detailing key steps for model building and analyses, and thus represents a quick guide and generic workflow for modern SDMs. Second, it introduces a structured format for documenting and communicating the models, ensuring transparency and reproducibility, facilitating peer review and expert evaluation of model quality, as well as meta-analyses. We detail all elements of ODMAP, and explain how it can be used for different model objectives and applications, and how it complements efforts to store associated metadata and define modelling standards. We illustrate its utility by revisiting nine previously published case studies, and provide an interactive web-based application to facilitate its use. We plan to advance ODMAP by encouraging its further refinement and adoption by the scientific community.     

Effects of sample size on the performance of species distribution models

A wide range of modelling algorithms is used by ecologists, conservation practitioners, and others to predict species ranges from point locality data. Unfortunately, the amount of data available is limited for many taxa and regions, making it essential to quantify the sensitivity of these algorithms to sample size. This is the first study to address this need by rigorously evaluating a broad suite of algorithms with independent presence–absence data from multiple species and regions. We evaluated predictions from 12 algorithms for 46 species (from six different regions of the world) at three sample sizes (100, 30, and 10 records). We used data from natural history collections to run the models, and evaluated the quality of model predictions with area under the receiver operating characteristic curve (AUC). With decreasing sample size, model accuracy decreased and variability increased across species and between models. Novel modelling methods that incorporate both interactions between predictor variables and complex response shapes (i.e. GBM, MARS-INT, BRUTO) performed better than most methods at large sample sizes but not at the smallest sample sizes. Other algorithms were much less sensitive to sample size, including an algorithm based on maximum entropy (MAXENT) that had among the best predictive power across all sample sizes. Relative to other algorithms, a distance metric algorithm (DOMAIN) and a genetic algorithm (OM-GARP) had intermediate performance at the largest sample size and among the best performance at the lowest sample size. No algorithm predicted consistently well with small sample size ( n  < 30) and this should encourage highly conservative use of predictions based on small sample size and restrict their use to exploratory modelling.     

物种分布模型理论研究进展

利用物种分布模型估计物种的真实和潜在分布区,已成为区域生态学与生物地理学中非常活跃的研究领域。然而,到目前为止,这项技术的理论基础仍然存在不足之处,一些关键的生态过程未能被有效纳入到物种分布模型的理论框架中,从而为解释物种分布模型预测的结果带来了诸多困惑。鉴于此,总结了物种分布模型的理论基础;系统探讨了物种分布模型与物种分布区的关系;特别指出了物种分布模型研究中存在的理论问题;重点阐述了物种分布模型未来的发展方向。研究认为,物种分布模型与生态位理论、源-库理论、种群动态理论、集合种群理论、进化理论等具有重要的联系;正确理解物种分布模型的预测结果与物种分布区的关系,有赖于对影响物种分布的3个主要因素(环境条件、物种相互作用与物种迁移能力)做出定量的分离;目前物种分布模型主要存在的问题是未能将物种的相互作用和物种的迁移能力有效纳入到模型的构建过程中;未来物种分布模型的发展应该加强模型背后理论框架的研究,并进一步加强整合物种相互作用过程、种群动态过程、迁移过程和物种进化过程等内容。研究还认为,从更高的理论层次模拟功能群和群落结构将是未来物种分布模型的重要发展方向。     

种间作用对物种分布模拟的影响及建模方法

物种分布模型(SDMs)通过量化物种分布和环境变量之间的关系,并将其外推到未知的景观单元,模拟、预测地理空间中生物的潜在分布,是生态学、生物地理学、保护生物学等研究领域的重要工具.然而,目前物种分布模型主要采用非生物因素作为预测变量,由于数据量化和建模表达困难,生物因素特别是种间作用在物种分布模型中常被忽略,将种间作用...     

Exon size distribution and the origin of introns

Since it was first recognised that eukaryotic genes are fragmented into coding segments (exons) separated by non-coding segments (introns), the reason for this phenomenon has been debated. There are two dominant theories: that the piecewise arrangement of genes allows functional protein domains, represented by exons, to recombine by shuffling to form novel proteins with combinations of functions; or that introns represent parasitic DNA that can infest the eukaryotic genome because it does not interfere grossly with the fitness of its host. Differing distributions of exon lengths are predicted by these two theories. In this paper we examine distributions of exon lengths for six different organisms and find that they offer empirical evidence that both theories may in part be correct.     

物种分布模型在海洋潜在生境预测的应用研究进展

海洋生物的栖息分布与环境要素的关联性一直是海洋生态学研究的热点之一.近年来,物种分布模型被广泛应用于预测海洋物种分布、潜在适宜性生境评价等研究,为保护海洋生物多样性、防治外来物种入侵及制定渔业管理措施等提供了一条有效途径.物种分布模型主要包括生境适宜性指数模型、机理模型和统计模型.本文对物种分布模型的理论基础进行了归纳和总结,回顾了物种分布模型在预测海洋物种潜在地理分布研究中的开发与应用,重点介绍了不同类型统计模型在海洋物种潜在分布预测中的研究实例.比较各种选取变量和模型验证方法,认为赤池信息准则对于选取模型变量具有优势,Kappa系数和受试者操作特征曲线下面积在验证模型精度中应用最广泛.阐述了物种分布模型存在的问题及未来发展趋势,随着海洋生物生理机制研究的进一步深入,机理模型将是今后物种分布模型发展的重点.     

中国北方5种栎属树木多度分布及其对未来气候变化的响应

植物分布与气候之间的关系是预估未来气候变化对生态系统影响的实现基础。以往的物种分布模型通常以物种的分布区或者分布点的物种存在数据作为物种分布的响应变量。相较于物种存在数据, 多度反映了一个物种占用资源并把资源分配给个体的能力, 更能衡量物种对区域生态系统的影响。该研究通过野外调查获取了华北及周边地区1 045个样方的栎属树木多度, 利用广义线性模型、广义加性模型和随机森林模型模拟栓皮栎(Quercus variabilis)、麻栎(Q. acutissima)、槲栎(Q. aliena)、锐齿槲栎(Q. aliena var. acuteserrata)和蒙古栎(Q. mongolica) 5个树种多度的地理分布及未来2个不同时期(2050年和2070年)的潜在分布。结果表明: 随机森林模型对5个栎属树种的多度的拟合结果要优于广义线性模型和广义加性模型; 典型浓度路径(RCP) 8.5下的5个栎属树种在未来两个时期的多度变化幅度都要大于RCP 2.6下的变化, 在超过一半面积的区域中麻栎、槲栎、锐齿槲栎和蒙古栎的多度减少, 其中内蒙古东北部和黑龙江北部地区是5种栎属植物多度减少的集中分布地区。未来气候变化背景下, 需要加强对这几个区域的监测与物种保护。     

作物水分供应订正函数的韦伯函数型

从作物水分生产潜力、潜在水分利用效率与水分满足率的关系及特点入手,引入水分供应订正函数的韦伯形式。与目前常见的水分供应订正函数形式相比,本文给出的韦伯形式的水分供应订正函数具有临界水分满足率和“旋回”特征,能较好地解释作物水分生产潜力与水分满足率之间的数量关系,模型的实用性较广。模型中位置参数表示临界水分满足率;尺度参数表示水分满足率的取值范围;形状参数决定水分供应订正函数的“峰度”和“偏斜度”。根据“图解法”可求取水分供应订正函数的相关参数。其参数也具有明确的生物学和物理学意义,参数值本身也较稳定,使模型在应用中可望具有良好的稳定性。     

Flexible species distribution modelling methods perform well on spatially separated testing data

Aim

To assess whether flexible species distribution models that perform well at nearby testing locations still perform strongly when evaluated on spatially separated testing data.

Location

Australian Wet Tropics (AWT), Ontario, Canada (CAN), north-east New South Wales, Australia (NSW), New Zealand (NZ), five countries of South America (SA), and Switzerland (SWI).

Time period

Most species data were collected between 1950 and 2000.

Major taxa studied

Birds, mammals, plants and reptiles.

Methods

We compared 10 species distribution modelling methods with varying flexibility in terms of the allowed complexity of their fitted functions [boosted regression trees (BRT), generalized additive model (GAM), multivariate adaptive regression splines (MARS), maximum entropy (MaxEnt), support vector machine (SVM), variants of generalized linear model (GLM) and random forest (RF), and an Ensemble model]. We used established practices for model selection to avoid overfitting, including parameter tuning in learning methods. Models were trained on presence–background data for 171 species and tested on presence–absence data. Training and testing data were separated using both random and spatial partitioning, the latter based on 75-km blocks. We calculated the average performance and mean rank of the methods (focussing on the area under the receiver operating characteristic and precision-recall gain curves, and correlation) and assessed the statistical significance of the differences between them.

Results

The ranking of methods did not change when evaluated on spatially separated testing data. Methods with the strongest predictive performance were nonparametric methods known to be flexible. An ensemble formed by averaging predictions of five pre-selected modelling methods was the best model in both random and spatial partitioning, followed by MaxEnt and a variant of random forest.

Main conclusions

Whilst some modellers expect methods limited to simple smooth functions to predict better spatially separated data, we found no evidence of that using blocks of 75 km. We conclude that flexible models that are tuned well enough to avoid overfitting are effective at predicting to spatially distinct areas.     

Trade-offs in covariate selection for species distribution models: a methodological comparison

Species distribution models (SDMs) are a common approach to describing species’ space-use and spatially-explicit abundance. With a myriad of model types, methods and parameterization options available, it is challenging to make informed decisions about how to build robust SDMs appropriate for a given purpose. One key component of SDM development is the appropriate parameterization of covariates, such as the inclusion of covariates that reflect underlying processes (e.g. abiotic and biotic covariates) and covariates that act as proxies for unobserved processes (e.g. space and time covariates). It is unclear how different SDMs apportion variance among a suite of covariates, and how parameterization decisions influence model accuracy and performance. To examine trade-offs in covariation parameterization in SDMs, we explore the attribution of spatiotemporal and environmental variation across a suite of SDMs. We first used simulated species distributions with known environmental preferences to compare three types of SDM: a machine learning model (boosted regression tree), a semi-parametric model (generalized additive model) and a spatiotemporal mixed-effects model (vector autoregressive spatiotemporal model, VAST). We then applied the same comparative framework to a case study with three fish species (arrowtooth flounder, pacific cod and walleye pollock) in the eastern Bering Sea, USA. Model type and covariate parameterization both had significant effects on model accuracy and performance. We found that including either spatiotemporal or environmental covariates typically reproduced patterns of species distribution and abundance across the three models tested, but model accuracy and performance was maximized when including both spatiotemporal and environmental covariates in the same model framework. Our results reveal trade-offs in the current generation of SDM tools between accurately estimating species abundance, accurately estimating spatial patterns, and accurately quantifying underlying species–environment relationships. These comparisons between model types and parameterization options can help SDM users better understand sources of model bias and estimate error.     

Correlation and process in species distribution models: bridging a dichotomy

Within the field of species distribution modelling an apparent dichotomy exists between process‐based and correlative approaches, where the processes are explicit in the former and implicit in the latter. However, these intuitive distinctions can become blurred when comparing species distribution modelling approaches in more detail. In this review article, we contrast the extremes of the correlative–process spectrum of species distribution models with respect to core assumptions, model building and selection strategies, validation, uncertainties, common errors and the questions they are most suited to answer. The extremes of such approaches differ clearly in many aspects, such as model building approaches, parameter estimation strategies and transferability. However, they also share strengths and weaknesses. We show that claims of one approach being intrinsically superior to the other are misguided and that they ignore the process–correlation continuum as well as the domains of questions that each approach is addressing. Nonetheless, the application of process‐based approaches to species distribution modelling lags far behind more correlative (process‐implicit) methods and more research is required to explore their potential benefits. Critical issues for the employment of species distribution modelling approaches are given, together with a guideline for appropriate usage. We close with challenges for future development of process‐explicit species distribution models and how they may complement current approaches to study species distributions.     

使用大熊猫数据评估Biomod2和MaxEnt分布预测模型的表现

物种分布模型是物种研究和保护者常用的工具.不同模型的预测结果可能相差很大,对研究者选择模型造成一定的难度.本研究使用大熊猫的实际分布数据评估了两种常见物种分布模型Biomod2和最大熵模型(MaxEnt)的表现,运用ROC曲线下面积(area under the curve,AUC)、真实技巧统计值(true skill statistics,TSS)、KAPPA统计量3种指标综合评估了两种模型预测结果的准确度.结果表明: 当使用的物种分布数据和模拟重复次数足够多的时候,两者都能够给出相当准确的预测.相对于MaxEnt,Biomod2的预测准确度更高,尤其是在物种分布点稀少的情况下.然而,Biomod2使用难度较大,运行时间较长,数据处理能力有限.研究者应基于对预测结果的误差要求来选择模型.在误差要求明确且两个模型都能满足误差要求时,建议使用MaxEnt,否则应优先考虑使用Biomod2.     

Recognition of protein complexation based on hydrophobicity distribution

The identification of the surface area able to generate the protein-protein complexation ligand and ion ligation is critical for the recognition of the biological function of particular proteins. The technique based on the analysis of the irregularity of hydrophobicity distribution is used as the criterion for the recognition of the interaction regions. Particularly, the exposure of hydrophobic residues on the surface of protein as well as the localization of the hydrophilic residues in the hydrophobic core is treated as potential area ready to interact with external molecules. The model based on the “fuzzy oil drop” approach treating the protein molecule as the drop of hydrophobicity concentrated in the central part of structure with the hydrophobicity close to zero on the surface according to 3-dimensional Gauss function. The comparison with the observed hydrophobicy in particular protein reveals some irregularities. These irregularities seem to represent the aim-oriented localization.