Climate change and plant distribution: local models predict high-elevation persistence

Mountain ecosystems will likely be affected by global warming during the 21st century, with substantial biodiversity loss predicted by species distribution models (SDMs). Depending on the geographic extent, elevation range, and spatial resolution of data used in making these models, different rates of habitat loss have been predicted, with associated risk of species extinction. Few coordinated across-scale comparisons have been made using data of different resolutions and geographic extents. Here, we assess whether climate change-induced habitat losses predicted at the European scale (10 × 10' grid cells) are also predicted from local-scale data and modeling (25 m × 25 m grid cells) in two regions of the Swiss Alps. We show that local-scale models predict persistence of suitable habitats in up to 100% of species that were predicted by a European-scale model to lose all their suitable habitats in the area. Proportion of habitat loss depends on climate change scenario and study area. We find good agreement between the mismatch in predictions between scales and the fine-grain elevation range within 10 × 10' cells. The greatest prediction discrepancy for alpine species occurs in the area with the largest nival zone. Our results suggest elevation range as the main driver for the observed prediction discrepancies. Local-scale projections may better reflect the possibility for species to track their climatic requirement toward higher elevations.     

Intimately linked or hardly speaking? The relationship between genotype and environmental gradients in a Louisiana Iris hybrid population

Several models of hybrid zone evolution predict the same spatial patterns of genotypic distribution whether or not structuring is due to environment-dependent or -independent selection. In this study, we tested for evidence of environment-dependent selection in an Iris fulva x Iris brevicaulis hybrid population by examining the distribution of genotypes in relation to environmental gradients. We selected 201 Louisiana Iris plants from within a known hybrid population (80 m x 80 m) and placed them in four different genotypic classes (I. fulva, I. fulva-like hybrid, I. brevicaulis-like hybrid and I. brevicaulis) based on seven species-specific random amplified polymorphic DNA (RAPD) markers and two chloroplast DNA haplotypes. Environmental variables were then measured. These variables included percentage cover by tree canopy, elevation from the high water mark, soil pH and percentage soil organic matter. Each variable was sampled for all 201 plants. Canonical discriminant analysis (CDA) was used to infer the environmental factors most strongly associated with the different genotypic groups. Slight differences in elevation (-0.5 m to +0.4 m) were important for distinguishing habitat distributions described by CDA, even though there were no statistical differences between mean elevations alone. I. brevicaulis occurred in a broad range of habitats, while I. fulva had a narrower distribution. Of all the possible combinations, I. fulva-like hybrids and I. brevicaulis-like hybrids occurred in the most distinct habitat types relative to one another. Each hybrid class was not significantly different from its closest parent with regard to habitat occupied, but was statistically unique from its more distant parental species. Within the hybrid genotypes, most, but not all, RAPD loci were individually correlated with environmental variables. This study suggests that, at a very fine spatial scale, environment-dependent selection contributed to the genetic structuring of this hybrid zone.     

Geographic range estimates and environmental requirements for the harpy eagle derived from spatial models of current and past distribution

Understanding species–environment relationships is key to defining the spatial structure of species distributions and develop effective conservation plans. However, for many species, this baseline information does not exist. With reliable presence data, spatial models that predict geographic ranges and identify environmental processes regulating distribution are a cost‐effective and rapid method to achieve this. Yet these spatial models are lacking for many rare and threatened species, particularly in tropical regions. The harpy eagle (Harpia harpyja) is a Neotropical forest raptor of conservation concern with a continental distribution across lowland tropical forests in Central and South America. Currently, the harpy eagle faces threats from habitat loss and persecution and is categorized as Near‐Threatened by the International Union for the Conservation of Nature (IUCN). Within a point process modeling (PPM) framework, we use presence‐only occurrences with climatic and topographical predictors to estimate current and past distributions and define environmental requirements using Ecological Niche Factor Analysis. The current PPM prediction had high calibration accuracy (Continuous Boyce Index = 0.838) and was robust to null expectations (pROC ratio = 1.407). Three predictors contributed 96% to the PPM prediction, with Climatic Moisture Index the most important (72.1%), followed by minimum temperature of the warmest month (15.6%) and Terrain Roughness Index (8.3%). Assessing distribution in environmental space confirmed the same predictors explaining distribution, along with precipitation in the wettest month. Our reclassified binary model estimated a current range size 11% smaller than the current IUCN range polygon. Paleoclimatic projections combined with the current model predicted stable climatic refugia in the central Amazon, Guyana, eastern Colombia, and Panama. We propose a data‐driven geographic range to complement the current IUCN range estimate and that despite its continental distribution, this tropical forest raptor is highly specialized to specific environmental requirements.     

珍稀濒危植物野生莲的适生分布区预测

基于野生莲（Nelumbo nucifera Gaertn.）136个分布点的数据和14个环境因子参数,运用规则集遗传算法（GARP）和最大熵（MaxEnt）两个生态位模型对他们在我国的适生分布区进行预测。结果显示：根据GARP和MaxEnt模型计算得到的ROC曲线下面积的AUC均值分别为0.861和0.964,其中MaxEnt模型的AUC值更大,预测结果更精准。MaxEnt模型预测结果表明,莲的最适分布区主要集中在四川、湖北、湖南等地的大部分地区,江西北部,以及黑龙江、辽宁、浙江、广东等地的小部分地区。刀切法（Jackknife）检测结果表明,影响莲适生分布区的主要环境因子包括：水汽压、海拔、年平均气温、多年平均降水量、最热季节平均温度、最冷季节平均温度、最干月降水量、最冷月最低温和最热月最高温等。适生区环境因子的统计分析结果显示,野生莲最适宜生长在海拔1~2216 m、年降水量丰富（1202.50 mm）、年均温约为16.19℃、最热月温度范围在24.60℃~35.10℃、最冷月均温不低于-0.53℃的地区。研究结果可为有效保护中国野生莲资源提供有利依据。     

Habitat suitability of Gymnocladus assamicus - A critically endangered plant of Arunachal Pradesh,India using machine learning and statistical modeling

The present study sought to identify the potential distribution range of critically endangered Gymnocladus assamicus in Arunachal Pradesh based on published data and field collection. We used the Maxent model to estimate the range of distribution and the result was then compared with three other models, i.e., the Generalized Linear Model (GLM), the Bioclim and the Random Forest model to assess the species' habitat suitability. A total of 23 different environmental variables were used, including bioclimatic ones, monthly minimum and maximum temperature, monthly precipitation and elevation data. The Maxent output listed 12 variables explaining 99.9% variation in the model. In comparison, Maxent showed the maximum region under habitat suitability criteria (1884.48 km²), followed by Random Forest (70.73 km²) and Bioclim (11.62 km²) model. Except for the Maxent model, suitable habitats predicted by other models are highly restricted within and across the study species' current distribution range. The average model prediction shows an expanded distribution range for the species up to Tawang which is the closest district of currently known distribution of the species in the state. Thus, the present study recognizes the importance of the geographic range of G. assamicus, a critically endangered species with very limited spatial distribution range and also provides some specific details to explore possible habitats for the species in new areas of potential occurrence in Arunachal Pradesh, India.     

Climate change induced elevational range shifts of Himalayan tree species

Global warming may force montane species to shift upward to keep pace with their shifting climate niche. How species differences in such distribution shifts depend on their elevational positions, elevation-dependent warming rates, and other environmental constraints, or plant functional traits is poorly understood. Here, we analyzed for 137 Himalayan tree species how distribution shifts vary with elevational niche positions, environmental constraints, and their functional traits. We developed ecological niche models using MaxEnt by combining species survey and botanical collections data with 19 environmental predictors. Species distributions were projected to 1985 and 2050 conditions, and elevational range parameters and distribution areas were derived. Under the worst-case RCP 8.5 scenario, species are predicted to shift, on average, 3 m/year in optimum elevation, and have 33% increase in distribution area. Highland species showed faster predicted elevational shifts than lowland species. Lowland and highland species are predicted to expand in distribution area in contrast to mid-elevation species. Tree species for which species distribution models are driven by responses to temperature, aridity, or soil clay content showed the strongest predicted upslope shifts. Tree species with conservative trait values that enable them to survive resource poor conditions (i.e., narrow conduits) showed larger predicted upslope shifts than species with wide conduits. The predicted average upslope shift in maximum elevation (8 m/year) is >2 times faster than the current observations indicating that many species will not be able to track climate change and potentially go extinct, unless they are supported by active conservation measures, such as assisted migration.     

The range implications of lizard traits in changing environments

Aim Most predictions of species ranges are based on correlating current species localities to environmental conditions. These correlative models do not explicitly include a species' biology. In contrast, some mechanistic models link traits to energetics and population dynamics to predict species distributions. These models enable one to ask whether considering a species' biology is important for predicting its range. I implement mechanistic models to investigate how a species' morphology, physiology and life history influence its range. Location North America. Methods I compare the mechanistic model predictions with those of correlative models for eight species of North American lizards in both current environments and following a uniform 3 °C temperature warming. I then examine the implications of superimposing habitat and elevation requirements on constraints associated with environmental tolerances. Results In the mechanistic model, species with a narrower thermal range for activity are both predicted and observed to have more restricted distributions. Incorporating constraints on habitat and elevation further restricts species distributions beyond areas that are thermally suitable. While correlative models generally outperform mechanistic models at predicting current distributions, the performance of mechanistic models improves when incorporating additional factors. In response to a 3 °C temperature warming, the northward range shifts predicted by the mechanistic model vary between species according to trait differences and are of a greater extent than those predicted by correlative models. Main conclusions These findings highlight the importance of species traits for understanding the dynamics of species ranges in changing environments. The analysis demonstrates that mechanistic models may provide an important complement to correlative models for predicting range dynamics, which may underpredict climate‐induced range shifts.     

Shifts in potential geographical distribution of Pterocarya stenoptera under climate change scenarios in China

Climate change poses a serious threat to biodiversity. Predicting the effects of climate change on the distribution of a species' habitat can help humans address the potential threats which may change the scope and distribution of species. Pterocarya stenoptera is a common fast‐growing tree species often used in the ecological restoration of riverbanks and alpine forests in central and eastern China. Until now, the characteristics of the distribution of this species' habitat are poorly known as are the environmental factors that influence its preferred habitat. In the present study, the Maximum Entropy Modeling (Maxent) algorithm and the Genetic Algorithm for Ruleset Production (GARP) were used to establish the models for the potential distribution of this species by selecting 236 sites with known occurrences and 14 environmental variables. The results indicate that both models have good predictive power. Minimum temperature of coldest month (Bio6), mean temperature of warmest quarter (Bio10), annual precipitation (Bio12), and precipitation of driest month (Bio14) were important environmental variables influencing the prediction of the Maxent model. According to the models, the temperate and subtropical regions of eastern China had high environmental suitability for this species, where the species had been recorded. Under each climate change scenario, climatic suitability of the existing range of this species increased, and its climatic niche expanded geographically to the north and higher elevation. GARP predicted a more conservative expansion. The projected spatial and temporal patterns of P. stenoptera can provide reference for the development of forest management and protection strategies.     

Predicting potential distribution of poorly known species with small database: the case of four‐horned antelope Tetracerus quadricornis on the Indian subcontinent

Information gaps on the distribution of data deficient and rare species such as four‐horned antelope (FHA) in Nepal may impair their conservation. We aimed to empirically predict the distribution of FHA in Nepal with the help of data from the Indian subcontinent. Additionally, we wanted to identify core areas and gaps within the reported range limits and to assess the degree of isolation of known Nepalese populations from the main distribution areas in India. The tropical part of the Indian subcontinent (65°–90° eastern longitude, 5°–30° northern latitude), that is, the areas south of the Himalayan Mountains. Using MaxEnt and accounting for sampling bias, we developed predictive distribution models from environmental and topographical variables, and known presence locations of the study species in India and Nepal. We address and discuss the use of target group vs. random background. The prediction map reveals a disjunct distribution of FHA with core areas in the tropical parts of central to southern–western India. At the scale of the Indian subcontinent, suitable FHA habitat area in Nepal was small. The Indo‐Gangetic Plain isolates Nepalese from the Indian FHA populations, but the distribution area extends further south than proposed by the current IUCN map. A low to intermediate temperature seasonality as well as low precipitation during the dry and warm season contributed most to the prediction of FHA distribution. The predicted distribution maps confirm other FHA range maps but also indicate that suitable areas exist south of the known range. Results further highlight that small populations in the Nepalese Terai Arc are isolated from the Indian core distribution and therefore might be under high extinction risk.     

基于MaxEnt模型的中国白杄分布格局及未来变化

白杄(Picea meyeri)1989年被评为内蒙古自治区Ⅱ级保护珍稀濒危植物。该研究基于白杄在中国地区的50条有效分布点记录和12个环境因子变量,利用MaxEnt模型和ArcGIS软件分析全新世中期、现代、2050年和2070年四个时期白杄在中国的潜在地理分布,通过环境因子的贡献率和刀切法检验确定限制现代潜在地理分布的主导因子,并利用响应曲线确定环境因子变量的适宜区间,以明确不同时期白杄潜在地理分布区域和面积,为白杄的引种以及保护管理提供依据。结果显示:(1)MaxEnt模型预测受试者工作曲线面积(AUC)为0.979,说明该模型预测的潜在分布精度准确,预测结果的可信度高。(2)影响白杄潜在分布的主要气候因子及其适宜生长范围为:海拔(1200~2300 m)、昼夜温差与年温差比值(25%~28%)、最湿月降雨量(90~145 mm)和年平均温度(0~5℃)。(3)现代白杄在中国的潜在地理分布总面积为103.56万km²,主要位于内蒙古中西部地区(九峰山、正蓝旗、多伦县)、山西省大部分地区(大石洞、五台山)以及河北省部分地区(雾灵山、塞罕坝)。(4)从全新世中期到现代气候条件下,白杄在内蒙古北部高纬度地区的潜在分布区面积减少,生存适宜度降低,内蒙古中部大部分最适生区丧失;2070年RCP2.6排放情景下,白杄在山西省、河北省等低纬度地区的适生区也基本丧失,与现代分布区相比,白杄在未来气候条件下的适生区缩小,并且向内蒙古东北方向迁移。研究表明,从全新世中期到2070年,白杄的潜在分布区面积逐渐缩小,且有向高纬度、高海拔地区迁移的趋势,其最适生区范围也向内蒙古东北地区移动。     

Predicting the distribution potential of an invasive frog using remotely sensed data in Hawaii

Aim Eleutherodactylus coqui (commonly known as the coqui) is a frog species native to Puerto Rico and non‐native in Hawaii. Despite its ecological and economic impacts, its potential range in Hawaii is unknown, making control and management efforts difficult. Here, we predicted the distribution potential of the coqui on the island of Hawaii. Location Puerto Rico and Hawaii. Methods We predicted its potential distribution in Hawaii using five biophysical variables derived from Moderate Resolution Imaging Spectroradiometer (MODIS) as predictors, presence/absence data collected from Puerto Rico and Hawaii and three classification methods – Classification Trees (CT), Random Forests (RF) and Support Vector Machines (SVM). Results Models developed separately using data from the native range and the invaded range predicted potential coqui habitats in Hawaii with high performance. Across the three classification methods, mean area under the ROC curve (AUC) was 0.75 for models trained using the native range data and 0.88 for models trained using the invaded range data. We achieved the highest AUC value of 0.90 using RF for models trained with invaded range data. Main conclusions Our results showed that the potential distribution of coquis on the island of Hawaii is much larger than its current distribution, with RF predicting up to 49% of the island as suitable coqui habitat. Predictions also show that most areas with an elevation between 0 and 2000 m are suitable coqui habitats, whereas the cool and dry high elevation areas beyond 2000 m elevation are unsuitable. Results show that MODIS‐derived biophysical variables are capable of characterizing coqui habitats in Hawaii.     

基于MaxEnt模型预测四川省松材线虫的潜在适生区

松材线虫Bursaphelenchus xylophilus是我国重要的林业检疫性有害生物之一,由其引发的松材线虫病已造成巨大的经济损失,严重阻碍了林业的健康发展。研究并明确松材线虫在四川省的潜在适生区,对四川省有关部门制定该病害的早期监测、预警及防控具有一定的参考意义。本文基于2009—2018年四川省林业有害生物普查数据中松材线虫病和松墨天牛Monochamus alternatus的实际地理分布数据(松材线虫病:n=208,松墨天牛:n=803)及19个环境变量数据,利用MaxEnt模型和Arc GIS对松材线虫在四川省的潜在分布区进行预测,并用ROC曲线分析法检测模型模拟精度、用刀切法检测变量的重要性及其适宜值。结果表明:松材线虫在四川省的潜在最佳适生区主要分布在宜宾市、广安市、达州市、自贡市、西昌市,以及乐山市和眉山市的交界区,面积为36 541 km^2;影响松材线虫分布的主要环境变量为最干季均温(适值范围1. 5～8. 0℃,最适值6. 4℃)、季节性降水变异系数(适值范围22. 5%～34. 0%,最适值34. 0%)、最冷月最低温(适值范围0. 4～2. 5℃,最适值1. 9℃)、海拔(适值范围250～5 500 m,最适值450 m)、年温差(适值范围5. 9～9. 1℃,最适值5. 9℃)和年降水量(适值范围64～135 mm,最适值68 mm)。     

Vanishing islands in the sky? A comparison of correlation- and mechanism-based forecasts of range dynamics for montane salamanders under climate change

Forecasting the effects of climate change on species and populations is a fundamental goal of conservation biology, especially for montane endemics which seemingly are under the greatest threat of extinction given their association with cool, high elevation habitats. Species distribution models (also known as niche models) predict where on the landscape there is suitable habitat for a species of interest. Correlative niche modeling, the most commonly employed approach to predict species' distributions, relies on correlations between species' localities and current environmental data. This type of model could spuriously forecast less future suitable habitat because species' current distributions may not adequately represent their thermal tolerance, and future climate conditions may not be analogous to current conditions. We compared the predicted distributions for three montane species of Plethodon salamanders in the southern Appalachian Mountains of North America using a correlative modeling approach and a mechanistic model. The mechanistic model incorporates species-specific physiology, morphology and behavior to predict an annual energy budget on the landscape. Both modeling approaches performed well at predicting the species' current distributions and predicted that all species could persist in habitats at higher elevation through 2085. The mechanistic model predicted more future suitable habitat than the correlative model. We attribute these differences to the mechanistic approach being able to model shifts in key range-limiting biological processes (changes in surface activity time and energy costs) that the correlative approach cannot. Choice of global circulation model (GCM) contributed significantly to distribution predictions, with a tenfold difference in future suitability based on GCM, indicating that GCM variability should be either directly included in models of species distributions or, indirectly, through the use of multi-model ensemble averages. Our results indicate that correlative models are over-predicting habitat loss for montane species, suggesting a critical need to incorporate mechanisms into forecasts of species' range dynamics.     

气候变化下考氏白盾蚧的潜在分布区预测

【目的】评估园林植物害虫考氏白盾蚧Pseudaulacaspis cockerelli当前和未来在全世界的潜在分布区,揭示未来气候变化下考氏白盾蚧的分布动态,明确气候环境因素对其潜在分布的影响。【方法】以考氏白盾蚧为研究对象,基于考氏白盾蚧在全球的118条有效地理分布记录和19个环境变量,运用优化的MaxEnt模型和ArcGIS软件,推测气候变化下当前、2050年和2070年考氏白盾蚧的潜在分布格局,采用响应曲线确定环境变量的适宜区间,定量确定考氏白盾蚧未来气候条件下潜在地理分布动态。【结果】MaxEnt模型运算的平均曲线下面积(area under the curve, AUC)值为0.7182,表明该预测模型的预测精度比较高。当前考氏白盾蚧潜在地理分布的总适生区面积约为2.73×10⁷ km²,其中高适生区面积大约为4.37×10⁶ km²,占潜在可入侵总面积的16％,该区域主要位于美国与巴西西南沿海地区,印度西部地区及西部沿海区域,孟加拉国,越南北部大部,中国西南大部及华东华中大部,以及日本南部地区;在未来气候条件下,伴随着CO₂浓度的升高,考氏白盾蚧的高适生面积将显著增加。影响考氏白盾蚧的潜在地理分布的主要环境变量为平均月温差、昼夜温差与年温差比、最湿季平均温度和降水季节性,其中昼夜温差与年温差比的贡献率最高,达到38.8%。【结论】本研究结果表明考氏白盾蚧适宜生境主要受平均月温差和昼夜温差与年温差比的影响。本研究为考氏白盾蚧的综合防治提供重要依据和数据支撑。     

Impact of climate change on vector transmission of Trypanosoma cruzi (Chagas, 1909) in North America

Climate change can influence the geographical range of the ecological niche of pathogens by altering biotic interactions with vectors and reservoirs. The distributions of 20 epidemiologically important triatomine species in North America were modelled, comparing the genetic algorithm for rule‐set prediction (GARP) and maximum entropy (MaxEnt), with or without topographical variables. Potential shifts in transmission niche for Trypanosoma cruzi (Trypanosomatida: Trypanosomatidae) (Chagas, 1909) were analysed for 2050 and 2070 in Representative Concentration Pathway (RCP) 4.5 and RCP 8.5. There were no significant quantitative range differences between the GARP and MaxEnt models, but GARP models best represented known distributions for most species [partial‐receiver operating characteristic (ROC) > 1]; elevation was an important variable contributing to the ecological niche model (ENM). There was little difference between niche breadth projections for RCP 4.5 and RCP 8.5; the majority of species shifted significantly in both periods. Those species with the greatest current distribution range are expected to have the greatest shifts. Positional changes in the centroid, although reduced for most species, were associated with latitude. A significant increase or decrease in mean niche elevation is expected principally for Neotropical 1 species. The impact of climate change will be specific to each species, its biogeographical region and its latitude. North American triatomines with the greatest current distribution ranges (Nearctic 2 and Nearctic/Neotropical) will have the greatest future distribution shifts. Significant shifts (increases or decreases) in mean elevation over time are projected principally for the Neotropical species with the broadest current distributions. Changes in the vector exposure threat to the human population were significant for both future periods, with a 1.48% increase for urban populations and a 1.76% increase for rural populations in 2050.     

基于生态位模型的艾比湖鹅喉羚生境评价

明确物种生境空间分布格局及其与环境因素的关系,对了解该物种的生境需求和适宜生境空间分布至关重要。生境评价和预测是对物种进行有效保护的基础。以鹅喉羚(Gazella subgutturosa)为研究对象,以其重要栖息地新疆博州艾比湖国家级湿地自然保护区为研究区域,选取115个鹅喉羚分布点数据和23个环境变量因子,应用MAXENT模型分析其生境空间分布及主要影响因子,划分了鹅喉羚在研究区域的适宜生境,并对它的栖息地特征进行了分析。探讨了鹅猴羚生境选择与环境因子的关系。结果表明:气温日较差是影响鹅喉羚生境分布的主要环境因子。植被类型,坡度和最干月降水量对艾比湖鹅喉羚的生境选择影响不大。除了温度和降水在内的19项生物气候变量是鹅猴羚选择生境的重要因素之外,海拔和坡向等地形特征也影响鹅猴羚的生境选择性。鹅喉羚的高度适宜生境区主要分布在研究区域的北部和东部,中度及低度适宜生境区则分布于高度适宜生境区的边缘,而非适宜生境区主要集中在西部地区。研究不仅提供了鹅喉羚在艾比湖的实际分布状况及其栖息地特征,也为鹅喉羚在栖息地方面的研究,即鹅猴羚的栖息地选择和环境因子的关系方面提供了一个重要的依据。     

Species Distribution Modelling predicts habitat suitability and reduction of suitable habitat under future climatic scenario for Sclerophrys perreti: A critically endangered Nigerian endemic toad

Sclerophrys perreti is a critically endangered Nigerian native frog currently imperilled by human activities. A better understanding of its potential distribution and habitat suitability will aid in conservation; however, such knowledge is limited for S. perreti. Herein, we used a species distribution model (SDM) approach with all known occurrence data (n = 22) from our field surveys and primary literature, and environmental variable predictors (19 bioclimatic variables, elevation and land cover) to elucidate habitat suitability and impact of climate change on this species. The SDM showed that temperature and precipitation were the predictors of habitat suitability for S. perreti with precipitation seasonality as the strongest predictor of habitat suitability. The following variable also had a significant effect on habitat suitability: temperature seasonality, temperature annual range, precipitation of driest month, mean temperature of wettest quarter and isothermality. The model predicted current suitable habitat for S. perreti covering an area of 1,115 km². However, this habitat is predicted to experience 60% reduction by 2050 owing to changes in temperature and precipitation. SDM also showed that suitable habitat exists in south-eastern range of the inselberg with predicted low impact of climate change compared to other ranges. Therefore, this study recommends improved conservation measures through collaborations and stakeholder's meeting with local farmers for the management and protection of S. perreti.     

Why Does Rhinopithecus bieti Prefer the Highest Elevation Range in Winter? A Test of the Sunshine Hypothesis

Environmental factors that affect spatiotemporal distribution patterns of animals usually include resource availability, temperature, and the risk of predation. However, they do not explain the counterintuitive preference of high elevation range in winter by the black-and-white snub-nosed monkey (Rhinopithecus bieti). We asked whether variation of sunshine along with elevations is the key driving force. To test this hypothesis, we conducted field surveys to demonstrate that there was a statistically significant pattern of high elevation use during winter. We then asked whether this pattern can be explained by certain environmental factors, namely temperature, sunshine duration and solar radiation. Finally, we concluded with a possible ecological mechanism for this pattern. In this study, we employed GIS technology to quantify solar radiation and sunshine duration across the monkey's range. Our results showed that: 1) R. bieti used the high altitude range between 4100-4400 m in winter although the yearly home range spanned from 3500-4500 m; 2) both solar radiation and sunshine duration increased with elevation while temperature decreased with elevation; 3) within the winter range, the use of range was significantly correlated with solar radiation and sunshine duration; 4) monkeys moved to the areas with high solar radiation and duration following a snowfall, where the snow melts faster and food is exposed earlier. We concluded that sunshine was the main factor that influences selection of high elevation habitat for R. bieti in winter. Since some other endotherms in the area exhibit similar winter distributional patterns, we developed a sunshine hypothesis to explain this phenomenon. In addition, our work also represented a new method of integrating GIS models into traditional field ecology research to study spatiotemporal distribution pattern of wildlife. We suggest that further theoretical and empirical studies are necessary for better understanding of sunshine influence on wildlife range use.     

用组合模型综合比较的方法分析气候变化对朱鹮潜在生境的影响

气候变化的不确定性和物种与环境关系的不确定性使气候变化生物学的研究充满变数。为了降低不确定性,人们开始用组合模型综合比较的方法研究物种对气候变化的响应。以朱鹮(Nipponia nippon)为研究对象,介绍组合模型综合比较方法的特点。朱鹮曾经高度濒危,目前种群大小在迅速恢复中;然而其分布区依旧狭小,气候变化可能是朱鹮面临的新威胁。应用BIOMOD模型中的9种模型,选择了每年的最低温和最高温、温度的季节性变异、每年的总降水量和降水的季节性变异共5个气候因子,依据WorldClim气候数据的CGCM2气候模型的A2a排放情形,计算了朱鹮当前(1950—2000年)的适宜生境和2020年、2050年、2080年3个阶段的潜在生境范围。结果表明朱鹮潜在生境将逐渐北移,生境中心脱离现在的保护区。因此,制定朱鹮的长期保护策略是必要的。9个模型在预测结果上、变量权重上和拟合优度的指标上都有差异,反映了模型本身的不确定性。气候变化的生物学效应比较复杂,应用多个模型进行综合比较,可以尽可能地减少模型所导致的误差。     

Assassin bugs of the subfamily Diaspidiinae (Heteroptera: Reduviidae): distribution and potential ecological niches

The prediction and definition of the conditions for the potentially suitable ecological niche of the subfamily Diaspidiinae was the main goal of this study. Our research was based on 283 specimens of all known species of assassin bugs belonging to the subfamily Diaspidiinae stored in European museum collections and a set of 21 environmental variables in the form of a 1 × 1 km grid covering Africa and Madagascar. Based on occurrence localities, as well as a digital elevation model and layer of the tree cover‐continuous fields, information about the distribution of each species is given. Using Maxent software, potentially useful ecological niches were modeled, which allowed for the creation of a map of the potential distribution of the members of this subfamily and for determining their climatic preferences. A jackknife test showed that annual precipitation, annual temperature range and tree cover‐continuous fields were the most important environmental variables affecting the distribution of the subfamily Diaspidiinae. An analysis of climatic preferences suggested that the representatives of the subfamily were linked mainly to the tropical climate. An analysis of environmental variables also showed that the subfamily preferred areas with herbaceous vegetation and some trees, and this preference is probably caused by the food preferences of their prey. On the basis of the museum data on the species occurrence, as well as ecological niche modeling methods, we provided new and valuable information on potentially suitable habitat and the possible range of distribution of the subfamily Diaspidiinae along with its climatic preferences.