Impact of the quality of climate models for modelling species occurrences in countries with poor climatic documentation: a case study from Bolivia

The quality of climate models has largely been overlooked as a possible source of uncertainty that may affect the outcomes of species distribution models, especially in the tropics, where comparatively few climatic stations are available. We compared the geographical discrepancies and potential conservation implications of using two different climate models (Saga and Worldclim) in combination with the species modelling approach Maxent in Bolivia. We estimated ranges of selected bird and fern species biogeographically restricted to either humid montane forest of the northern Bolivian Andes or seasonal dry tropical forests (in the Andes and southern lowlands). Saga and Worldclim predicted roughly similar climate patterns of temperature that were significantly correlated. Precipitation layers of both climate models were also roughly similar, but showed important differences. Species ranges estimated with Worldclim and Saga likewise produced different results. Ranges of species endemic to humid montane forests estimated with Saga had higher AUC (Area under the curve) values than those estimated with Worldclim, which for example predicted the occurrence of humid montane forest bird species near Lake Titicaca, an area that is clearly unsuitable for these species. Likewise, Worldclim overpredicted the occurrence of fern and bird species in the lowlands of the Chapare region and well south of the Andean Elbow, where more seasonal biomes occur. By contrast, Saga predictions were coherent with the known distribution of humid montane forests in the northern Bolivian Andes. Estimated ranges of species endemic to seasonal dry tropical forests predicted with Saga and Worldclim were not statistically different in most cases. However, detailed comparisons revealed that Saga was able to distinguish fragments of seasonal dry tropical forests in rain-shadow valleys of the northern Bolivian Andes, whereas Worldclim was not. These differences highlight the neglected influence of climate layers on modelling results and the importance of using the most accurate climate data available when modelling species distributions.     

Incorporating low-resolution historic species location data decreases performance of distribution models

Developing robust species distribution models is important as model outputs are increasingly being incorporated into conservation policy and management decisions. A largely overlooked component of model assessment and refinement is whether to include historic species occurrence data in distribution models to increase the data sample size. Data of different temporal provenance often differ in spatial accuracy and precision. We test the effect of inclusion of historic coarse-resolution occurrence data on distribution model outputs for 187 species of birds in Australian tropical savannas. Models using only recent (after 1990), fine-resolution data had significantly higher model performance scores measured with area under the receiver operating characteristic curve (AUC) than models incorporating both fine- and coarse-resolution data. The drop in AUC score is positively correlated with the total area predicted to be suitable for the species (R² = 0.163-0.187, depending on the environmental predictors in the model), as coarser data generally leads to greater predicted areas. The remaining unexplained variation is likely to be due to the covariate errors resulting from resolution mismatch between species records and environmental predictors. We conclude that decisions regarding data use in species distribution models must be conscious of the variation in predictions that mixed-scale datasets might cause.     

最大熵(Maxent)模型在物种栖息地研究中的应用

最大熵(Maxent)模型是以生态位理论为基础的新兴研究领域,它通过有限的物种分布点数据及其相关的环境信息组成训练样本,利用基于数据驱动的机器学习理论分析推算出物种的生态需求,然后将运算结果投射至不同的时间和空间中预测物种的潜在分布和实际分布.近年来,该模型在生态学和生物多样性保护等研究中越发重要,文章介绍了Maxent模型的基本原理,从物种栖息地需求分析、气候变化对物种分布的影响、物种入侵监测以及自然保护区的选择和规划设计等方面阐述Maxent模型在物种栖息地研究中的应用.     

Designing environmental monitoring networks to measure extremes

This paper discusses challenges arising in the design of networks for monitoring extreme values over the domain of a random environmental space-time field {X _ij } i = 1, . . . , I denoting site and j = 1, . . . denoting time (e.g. hour). The field of extremes for time span r over site domain i = 1, . . . ,I is given by \(\{Y_{i(r+1)}=\max_{j=k}^{k+n-1} X_{ij}\}\) for k = 1 + rn, r = 0, . . . ,. Such networks must not only measure extremes at the monitored sites but also enable their prediction at the non-monitored ones. Designing such a network poses special challenges that do not seem to have been generally recognized. One of these problems is the loss of spatial dependence between site responses in going from the environmental process to the field of extremes it generates. In particular we show empirically that the intersite covariance Cov(Y _i(r+1),Y _i′(r+1)) can generally decline toward zero as r increases, for site pairs i ≠ i′. Thus the measured extreme values may not predict the unmeasured ones very precisely. Consequently high levels of pollution exposure of a sensitive group (e.g. school children) located between monitored sites may be overlooked. This potential deficiency raises concerns about the adequacy of air pollution monitoring networks whose primary role is the detection of noncompliance with air quality standards based on extremes designed to protect human health. The need to monitor for noncompliance and thereby protect human health, points to other issues. How well do networks designed to monitor the field monitor their fields of extremes? What criterion should be used to select prospective monitoring sites when setting up or adding to a network? As the paper demonstrates by assessing an existing network, the answer to the first question is not well, at least in the case considered. To the second, the paper suggests a variety of plausible answers but shows through a simulation study, that they can lead to different optimum designs. The paper offers an approach that circumvents the dilemma posed by the answer to the second question. That approach models the field of extremes (suitably transformed) by a multivariate Gaussian-Inverse Wishart hierarchical Bayesian distribution. The adequacy of this model is empirically assessed in an application by finding the relative coverage frequency of the predictive credibility ellipsoid implied by its posterior distribution. The favorable results obtained suggest this posterior adequately describes that (transformed) field. Hence it can form the basis for designing an appropriate network. Its use is demonstrated by a hypothetical extension of an existing monitoring network. That foundation in turn enables a network to be designed of sufficient density (relative to cost) to serve its regulatory purpose.     

Incorporating fragmentation and non‐native species into distribution models to inform fluvial fish conservation

Fluvial fishes face increased imperilment from anthropogenic activities, but the specific factors contributing most to range declines are often poorly understood. For example, the range of the fluvial‐specialist shoal bass (Micropterus cataractae) continues to decrease, yet how perceived threats have contributed to range loss is largely unknown. We used species distribution models to determine which factors contributed most to shoal bass range loss. We estimated a potential distribution based on natural abiotic factors and a series of currently occupied distributions that incorporated variables characterizing land cover, non‐native species, and river fragmentation intensity (no fragmentation, dams only, and dams and large impoundments). We allowed interspecific relationships between non‐native congeners and shoal bass to vary across fragmentation intensities. Results from the potential distribution model estimated shoal bass presence throughout much of their native basin, whereas models of currently occupied distribution showed that range loss increased as fragmentation intensified. Response curves from models of currently occupied distribution indicated a potential interaction between fragmentation intensity and the relationship between shoal bass and non‐native congeners, wherein non‐natives may be favored at the highest fragmentation intensity. Response curves also suggested that >100 km of interconnected, free‐flowing stream fragments were necessary to support shoal bass presence. Model evaluation, including an independent validation, suggested that models had favorable predictive and discriminative abilities. Similar approaches that use readily available, diverse, geospatial data sets may deliver insights into the biology and conservation needs of other fluvial species facing similar threats.     

全球变化背景下南方红豆杉地域分布变化

气候是影响植物栖息地的重要因素之一,预测气候变化对植物潜在分布范围变动的影响,对促进植物资源的可持续利用具有重要意义。基于最大熵Maxent模型结合11个环境变量,预测2050s三种气候情景下（RCP2.6、RCP4.5和RCP8.5）南方红豆杉（Taxus chinensis var. mairei）在中国的潜在地理分布状况,分析影响其分布的主要因素,探讨其分布格局的改变对我国亚热带北界的指示意义。结果表明：（1）南方红豆杉的适宜栖息地（生境指数P >0.2）主要分布在我国亚热带暖温带季风区,绝大部分核心栖息地（生境指数P >0.6）分布在秦岭大巴山以南地区;（2）Jackknife测试结果显示,最冷季降水量（bio19）、气温平均日较差（bio2）、气温年变化范围（bio7）、最暖季平均温度（bio10）和海拔（Elev）对南方红豆杉空间分布适宜性影响最大; （3）随气候变化,2050s南方红豆杉有沿纬度向北和海拔向上迁移的趋势,并且我国亚热带北界受气候变化的影响将逐渐向北移动。     

Spatio-temporal dynamics of malaria expansion under climate change in semi-arid areas of Ethiopia

ABSTRACT

Malaria is a leading and severe disease in Ethiopia, particularly like the Tigray region. The main objectives of the study were to model the influence of climate change on malaria transmission in Tigray and identify environmental variables that contribute to malaria. Aiming these objectives, Kafta Humera, Raya Azebo and Laelay Adiabo districts were purposively selected based on their malaria prevalence. Two hundred and nine mosquito occurrence points were collected from the study area. Collected occurrence points, altitude and 19 bioclimatic variables were run in Maxent software. Malaria transmission was simulated for themiddle and end of the twenty-first century using two representative concentration pathways (RCP4.5 and RCP8.5) scenarios driving ensemble of three general circulation models. The results show that the area suitable for malaria transmission is simulated to increase by 93.8% (RCP4.5) and 113.9% (RCP8.5) by mid-century and by 161% (RCP4.5) and 149% (RCP8.5) by the end of the twenty-first century, when compared with the historical baseline. This indicates that the area suitable for malaria transmission is simulated to increase due to climate change over the region. Therefore, the study recommends well prevention and control of malaria to ensure the health of people.     

大香格里拉地区植被空间分布的环境特征

以植被种类丰富的大香格里拉地区为研究区,利用气象站点数据、数字高程数据(DEM)以及该地区植被分布图,通过最大熵模型(Maxent)和ArcGIS软件的空间分析功能,研究该地区典型植被空间分布的环境特征,分析了大香格里拉地区植被分布的气候特征和地形特征,量化了植被空间分布的气候范围,并根据二者的关系特征,得到了植被与气候因子关系模型。同时,界定了不同植被分布的海拔、坡度、坡向范围。发现大香格里拉地区影响不同植被空间分布的气候因子不同,影响草甸分布的是多年平均最低气温和多年7月平均气温,影响针叶林分布的是多年平均最低气温和多年平均日照时数,影响灌丛分布的较分散。同时不同植被的海拔分布界限和坡度不同,坡向对植被分布的影响不明显。研究结果可为该地区植被保护和管理,以及植被的气候变化响应提供基础参考。     

An evaluation of three statistical methods used to model resource selection

The performance of statistical methods for modeling resource selection by animals is difficult to evaluate with field data because true selection patterns are unknown. Simulated data based on a known probability distribution, though, can be used to evaluate statistical methods. Models should estimate true selection patterns if they are to be useful in analyzing and interpreting field data. We used simulation techniques to evaluate the effectiveness of three statistical methods used in modeling resource selection. We generated 25 use locations per animal and included 10, 20, 40, or 80 animals in samples of use locations. To simulate species of different mobility, we generated use locations at four levels according to a known probability distribution across DeSoto National Wildlife Refuge (DNWR) in eastern Nebraska and western Iowa, USA. We either generated 5 random locations per use location or 10,000 random locations (total) within 4 predetermined areas around use locations to determine how the definition of availability and the number of random locations affected results. We analyzed simulated data using discrete choice, logistic-regression, and a maximum entropy method (Maxent). We used a simple linear regression of estimated and known probability distributions and area under receiver operating characteristic curves (AUC) to evaluate the performance of each method. Each statistical method was affected differently by number of animals and random locations used in analyses, level at which selection of resources occurred, and area considered available. Discrete-choice modeling resulted in precise and accurate estimates of the true probability distribution when the area in which use locations were generated was ≥ the area defined to be available. Logistic-regression models were unbiased and precise when the area in which use locations were generated and the area defined to be available were the same size; the fit of these models improved with increased numbers of random locations. Maxent resulted in unbiased and precise estimates of the known probability distribution when the area in which use locations were generated was small (home-range level) and the area defined to be available was large (study area). Based on AUC analyses, all models estimated the selection distribution better than random chance. Results from AUC analyses, however, often contradicted results of the linear regression method used to evaluate model performance. Discrete-choice modeling was best able to estimate the known selection distribution in our study area regardless of sample size or number of random locations used in the analyses, but we recommend further studies using simulated data over different landscapes and different resource metrics to confirm our results. Our study offers an approach and guidance for others interested in assessing the utility of techniques for modeling resource selection in their study area.