Habitat distribution modelling,under the present climatic scenario,of the threatened endemic Iberian Delphinium fissum subsp. sordidum (Ranunculaceae) and implications for its conservation

We modelled the potential habitat of a threatened species D. fissum subsp. sordidum, an endemic hemicryptophyte with a disjunct distribution in the Iberian Peninsula. Maxent was used to predict the subspecies habitat suitability by relating field sample-based distributional information with environmental and topographic variables. Our results suggest that the model performed well, predicting with high accuracy the current distribution of the species. The variables that most contributed to the model were Mean Temperature of Wettest Quarter (MTWtQ), Precipitation of Warmest Quarter (PWmQ), Temperature Annual Range (TAR) and Slope (Slo). These variables are biological significant for the taxon, as they have decisive influence in the critical stages of germination and fruiting. The current and potential distributional areas identified by the model fall mainly in regions with some degree of environmental protection, with some exceptions. A recovery plan for the species should be considered. Species Distribution Modelling cannot substitute long-term monitoring programmes, yet it is a useful tool for identifying appropriate areas of taxon occurrence, and thus allow for efficient use of the economic and human resources.     

The distribution of cultivated species of Porophyllum (Asteraceae) and their wild relatives under climate change

The geographic distribution of plant species is already being affected by climate change. Cropping patterns of edible plant species and their wild relatives will also be affected, making it important to predict possible changes to their distributions in the future. Currently, species distribution models are valuable tools that allow the estimation of species’ potential distributions, in the recent past as well as during other time spans for which climate data have been obtained. With the aim of evaluating how species distributions respond to current and future climate changes, in this work species distribution models were generated for two cultivated species of the Porophyllum genus (Asteraceae), known commonly as ‘pápalos' or ‘pápaloquelites', as well as their Mexican wild relatives, at five points in time (21,000 years ago, present, 2020, 2050, and 2080). Using a database of 1442 entries for 16 species of Porophyllum and 19 environmental variables, species distribution models were constructed for each time period using the Maxent modelling algorithm; those constructed for the future used a severe climate change scenario. The results demonstrate contrasting effects between the two cultivated species; for P. linaria, the future scenario suggests a decrease in distribution area, while for P. macrocephalum distribution is predicted to increase. Similar trends are observed in their wild relatives, where 11 species will tend to decrease in distribution area, while three are predicted to increase. It is concluded that the most important agricultural areas where the cultivated species are grown will not be greatly affected, while the areas inhabited by the wild species will. However, while the results suggest that climate change will affect the distribution of the cultivated species in contrasting ways, evaluations at finer scales are recommended to clarify the impact within cultivation zones.     

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.     

Confronting species distribution model predictions with species functional traits

Species distribution models are valuable tools in studies of biogeography, ecology, and climate change and have been used to inform conservation and ecosystem management. However, species distribution models typically incorporate only climatic variables and species presence data. Model development or validation rarely considers functional components of species traits or other types of biological data. We implemented a species distribution model (Maxent) to predict global climate habitat suitability for Grass Carp (Ctenopharyngodon idella). We then tested the relationship between the degree of climate habitat suitability predicted by Maxent and the individual growth rates of both wild (N = 17) and stocked (N = 51) Grass Carp populations using correlation analysis. The Grass Carp Maxent model accurately reflected the global occurrence data (AUC = 0.904). Observations of Grass Carp growth rate covered six continents and ranged from 0.19 to 20.1 g day⁻¹. Species distribution model predictions were correlated (r = 0.5, 95% CI (0.03, 0.79)) with observed growth rates for wild Grass Carp populations but were not correlated (r = −0.26, 95% CI (−0.5, 0.012)) with stocked populations. Further, a review of the literature indicates that the few studies for other species that have previously assessed the relationship between the degree of predicted climate habitat suitability and species functional traits have also discovered significant relationships. Thus, species distribution models may provide inferences beyond just where a species may occur, providing a useful tool to understand the linkage between species distributions and underlying biological mechanisms.     

Challenges and perspectives for species distribution modelling in the neotropics

The workshop 'Species distribution models: applications, challenges and perspectives' held at Belo Horizonte (Brazil), 29-30 August 2011, aimed to review the state-of-the-art in species distribution modelling (SDM) in the neotropical realm. It brought together researchers in ecology, evolution, biogeography and conservation, with different backgrounds and research interests. The application of SDM in the megadiverse neotropics-where data on species occurrences are scarce-presents several challenges, involving acknowledging the limitations imposed by data quality, including surveys as an integral part of SDM studies, and designing the analyses in accordance with the question investigated. Specific solutions were discussed, and a code of good practice in SDM studies and related field surveys was drafted.     

Predicting species distribution: offering more than simple habitat models

In the last two decades, interest in species distribution models (SDMs) of plants and animals has grown dramatically. Recent advances in SDMs allow us to potentially forecast anthropogenic effects on patterns of biodiversity at different spatial scales. However, some limitations still preclude the use of SDMs in many theoretical and practical applications. Here, we provide an overview of recent advances in this field, discuss the ecological principles and assumptions underpinning SDMs, and highlight critical limitations and decisions inherent in the construction and evaluation of SDMs. Particular emphasis is given to the use of SDMs for the assessment of climate change impacts and conservation management issues. We suggest new avenues for incorporating species migration, population dynamics, biotic interactions and community ecology into SDMs at multiple spatial scales. Addressing all these issues requires a better integration of SDMs with ecological theory.     

Spatial distribution patterns of Littoraria species in Hong Kong mangroves

Variation in the abundance and distribution of two species of mangrove littorinid was investigated using a nested sampling design at different spatial scales, in the two dominant seasons, in Hong Kong. The abundance of Littoraria melanostoma, which was less abundant than L. ardouiniana, showed large-scale spatial variation whilst abundance of L. ardouiniana varied on both large and smaller spatial scales and interacted with seasons, indicating that the abundance of this species varied between mangroves in summer and winter. Small-scale variation suggested a patchy distribution of littorinids within a mangrove, whilst the large-scale variation might reflect changes in physical factors possibly associated with habitat fragmentation. Investigation of the vertical distribution of the littorinids on the mangrove trees revealed that L. melanostoma were located at a lower overall level than L. ardouiniana. This zonation pattern may be a result of the morphological differences of the two species, as L. melanostoma has a thick shell, whilst L. ardouiniana has a thinner, but colour polymorphic shell. Both species of mangrove littorinid showed patchy distributions at a variety of scales and these patterns highlight the importance of using a hierarchical sampling approach when investigating spatially fragmented habitats.     

松林中焦艺夜蛾幼虫的空间格局

应用聚集度指标和回归分析等方法,测定了焦艺夜蛾幼虫的空间格局。结果表明,焦艺夜蛾幼虫在马尾松林内呈聚集型分布,分布的基本成分为个体群,个体之间相互吸引;用马占山重新解释的Taylor幂法则分析表明,焦艺夜蛾幼虫的空间格局归属于“聚集度逆密度制约型”。     

To Be Or Not to Be? Variable selection can change the projected fate of a threatened species under future climate

Species distribution models (SDMs) are commonly used to project future changes in the geographic ranges of species, to estimate extinction rates and to plan biodiversity conservation. However, these models can produce a range of results depending on how they are parameterized, and over‐reliance on a single model may lead to overconfidence in maps of future distributions. The choice of predictor variable can have a greater influence on projected future habitat than the range of climate models used. We demonstrate this in the case of the Ptunarra Brown Butterfly, a species listed as vulnerable in Tasmania, Australia. We use the Maxent model to develop future projections for this species based on three variable sets; all 35 commonly used so‐called ‘bioclimatic’ variables, a subset of these based on expert knowledge, and a set of monthly climate variables relevant to the species’ primary activity period. We used a dynamically downscaled regional climate model based on three global climate models. Depending on the choice of variable set, the species is projected either to experience very little contraction of habitat or to come close to extinction by the end of the century due to lack of suitable climate. The different conclusions could have important consequences for conservation planning and management, including the perceived viability of habitat restoration. The output of SDMs should therefore be used to define the range of possible trajectories a species may be on, and ongoing monitoring used to inform management as changes occur.     

东北地区5个物种潜在栖息地变化与优化保护规划

气候变化广泛影响着物种多样性及其分布变迁。优化模型模拟结果,获取气候变化影响下的优先保护区域将为制定应对气候变化的物种保护政策或行动提供理论依据,提升保护绩效。选取东北地区五种代表性动物,包括黑熊(Ursus thibetanus)、驼鹿(Alces alces)、水獭(Lutra lutra)、紫貂(Martes zibellina)及黑嘴松鸡(Tetrao parvirostris);结合最大熵模型(Maxent)模拟在不同RCP情景下未来3个年代(2030s,2050s,2070s)的物种潜在栖息地。根据九个常用气候模式的评价结果,获取东北地区合适的气候模式,了解气候变化对物种潜在栖息地的影响,同时开展物种保护规划,识别保护空缺,为应对气候变化、保持生物多样性提供支持。结果显示,在气候变化背景下物种潜在栖息地面积整体呈现下降趋势,但不同气候模式之间存在差异;评价结果推荐CCSM4、Nor ESM1-M、Had GEM2-AO及GFDL-CM3气候模式,推荐在东北地区使用以上气候模式进行物种未来潜在分布的研究。5个物种潜在栖息地平均面积变化率分别为-62.16%,-73.93%,-78.46%(2030s,2050s,2070s)。综合5个重点保护物种的保护优先区,大兴安岭的呼中、汗马与额尔古纳国家级自然保护区,延边地区的天佛指山、老爷岭东北虎、珲春东北虎与汪清原麝国家级自然保护区,长白山国家级自然保护区是气候变化下物种保护的热点区域。     

蔬菜花期蓟马的种群动态与空间分布研究

西花蓟马是近年来在我国局部地区暴发成灾的重要外来入侵害虫,有关西花蓟马入侵对本地蓟马种群动态、空间分布及优势种影响的报道较少。对云南省昆明市近郊蔬菜花期的蓟马种群动态和空间分布研究表明,蔬菜上的蓟马种类主要是西花蓟马Frankliniella occidentalis(Pergande)、花蓟马F.intonsa(Trybom)、棕榈蓟马T.palmi(Karny)和端大蓟马Megalurothrips distalis(Karny);不同蔬菜上的蓟马优势种存在一定差异,其中辣椒和茼蒿上的蓟马优势种为西花蓟马;韭菜、茄子和四季豆上的蓟马优势种分别为花蓟马、棕榈蓟马和端大蓟马。各蔬菜上的蓟马种群数量以花期为多,盛花期达最大值,其中茄子花上的蓟马成虫平均虫口密度最高,为14.93头/朵。利用聚集度指标进行空间分布检测表明,不同蔬菜上蓟马成虫的空间分布型均为聚集分布,且聚集程度随密度的增加而增大。本研究可为深入探讨西花蓟马对本地蓟马的竞争取代机制积累资料,同时为西花蓟马的综合治理奠定理论基础。     

Estimating demographic models for the range dynamics of plant species

Aims To better understand how demographic processes shape the range dynamics of woody plants (in this case, Proteaceae), we introduce a likelihood framework for fitting process‐based models of range dynamics to spatial abundance data. Location The fire‐prone Fynbos biome (Cape Floristic Region, South Africa). Methods Our process‐based models have a spatially explicit demographic submodel (describing dispersal, reproduction, mortality and local extinction) as well as an observation submodel (describing imperfect detection of individuals), and are constrained by species‐specific predictions of habitat distribution models and process‐based models for seed dispersal by wind. Free model parameters were varied to find parameter sets with the highest likelihood. After testing this approach with simulated data, we applied it to eight Proteaceae species that differ in breeding system (monoecy versus dioecy) and adult fire survival. We assess the importance of Allee effects and negative density dependence for range dynamics, by using the Akaike information criterion to select between alternative models fitted for the same species. Results The best model for all dioecious study species included Allee effects, whereas this was true for only one of four monoecious species. As expected, sprouters (in which adults survive fire) were estimated to have lower rates of reproduction and catastrophic population extinction than related non‐sprouters. Overcompensatory population dynamics seem important for three of four non‐sprouters. We also found good quantitative agreement between independent data and most estimates of reproduction, carrying capacity and extinction probability. Main conclusions This study shows that process‐based models can quantitatively describe how large‐scale abundance distributions arise from the movement and interaction of individuals. It stresses links between the life history, demography and range dynamics of Proteaceae: dioecious species seem more susceptible to Allee effects which reduce migration ability and increase local extinction risk, and sprouters seem to have high persistence of established populations, but their low reproduction limits habitat colonization and migration.     

Predicting species distributions in poorly-studied landscapes

Conservationists are increasingly relying on distribution models to predict where species are likely to occur, especially in poorly-surveyed but biodiverse areas. Modeling is challenging in these cases because locality data necessary for model formation are often scarce and spatially imprecise. To identify methods best suited to modeling in these conditions, we compared the success of three algorithms (Maxent, Mahalanobis Typicalities and Random Forests) at predicting distributions of eight bird and eight mammal species endemic to the eastern slopes of the central Andes. We selected study species to have a range of locality sample sizes representative of the data available for endemic species of this region and also that vary in their distribution characteristics. We found that for species that are known from moderate numbers (N = 38–94) of localities, the three methods performed similarly for species with restricted distributions but Maxent and Random Forests yielded better results for species with wider distributions. For species with small numbers of sample localities (N = 5–21), Maxent produced the most consistently successful results, followed by Random Forests and then Mahalanobis Typicalities. Because evaluation statistics for models derived from few localities can be suspect due to the poor spatial representation of the evaluation data, we corroborated these results with review by scientists familiar with the species in the field. Overall, Maxent appears to be the most capable method for modeling distributions of Andean bird and mammal species because of the consistency of results in varying conditions, although the other methods have strengths in certain situations. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Uncertainty in ensemble forecasting of species distribution

Species distribution modelling has been widely applied in order to assess the potential impacts of climate change on biodiversity. Many methodological decisions, taken during the modelling process and forecasts, may, however, lead to a large variability in the assessment of future impacts. Using measures of species range change and turnover, the potential impacts of climate change on French stream fish species and assemblages were evaluated. Our main focus was to quantify the uncertainty in the projections of these impacts arising from four sources of uncertainty: initial datasets (Data), statistical methods [species distribution models (SDM)], general circulation models (GCM), and gas emission scenarios (GES). Several modalities of the aforementioned uncertainty sources were combined in an ensemble forecasting framework resulting in 8400 different projections. The variance explained by each source was then extracted from this whole ensemble of projections. Overall, SDM contributed to the largest variation in projections, followed by GCM, whose contribution increased over time equalling almost the proportion of variance explained by SDM in 2080. Data and GES had little influence on the variability in projections. Future projections of range change were more consistent for species with a large geographical extent (i.e., distribution along latitudinal or stream gradients) or with restricted environmental requirements (i.e., small thermal or elevation ranges). Variability in projections of turnover was spatially structured at the scale of France, indicating that certain particular geographical areas should be considered with care when projecting the potential impacts of climate change. The results of this study, therefore, emphasized that particular attention should be paid to the use of predictions ensembles resulting from the application of several statistical methods and climate models. Moreover, forecasted impacts of climate change should always be provided with an assessment of their uncertainty, so that management and conservation decisions can be taken in the full knowledge of their reliability.     

The shifts in species composition and ecological modelling in hydrobiology

Ecosystems have an enormous flexibility to meet changes in external factors and still maintain their functions. If present species are not able to cope with the conditions, given by the external factors, new species are waiting in the wings ready to take over. It is a serious shortcoming of our present ecological models, that they are not able to describe these changes in species composition. However, it seems that the thermodynamic function exergy may be used as goal function in ecological models to incorporate the flexibility of real ecosystems and the selection of species into our models of ecosystems. The application of exergy in modelling may be considered a translation of Darwin's selection into thermodynamics. The theoretical basis for the application of exergy as goal function is presented, and applications of exergy in ecological models are illustrated by several case studies.     

夜郎湖水库水体不同形态汞的时空分布

于2006年7月(夏季)、2007年1月(冬季)和3月(春季)采集了贵州省夜郎湖水库水样,研究了不同形态汞(总汞、溶解态汞、颗粒态汞)的时空分布特征及其影响因素.结果表明,夏季水体总汞、溶解态汞、颗粒态汞平均含量分别为4.48±2.59、2.37±1.40、2.11±1.86 ng·L-1,均显著高于冬季和春季(P＜0.001),而冬春2季不同形态汞含量无明显差异.水质参数悬浮颗粒物(SPM)和硝酸盐(NO-3)与不同形态汞之间均存在显著的正相关关系,表明这些参数对于不同形态汞的季节分布起着重要作用.夏季农业耕作活动相对活跃,表层土壤的扰动增加,雨水冲刷农田土壤,带进大量的外源颗粒物,致使夜郎湖水体夏季总汞水平较高.空间分布表明,夜郎湖水库夏季总汞平均浓度从水库入库河流至大坝方向、出库河流呈现总体下降的分布趋势,但水体各采样剖面没有明显的分布规律.     

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.     

松突圆蚧及友恩蚜小蜂空间分布型和种群消长动态研究

通过对广东省试验林区内松突圆蚧Hemiberlesia pitysophila Takagi及其本地种寄生蜂友恩蚜小蜂Encarsia amicula Viggiani et Ren林间空间分布型和种群消长动态的调查研究发现:两者空间分布型相吻合,都为均匀分布;两者在林间全年种群消长曲线均呈"S"型变化。松突圆蚧种群密度高峰期出现在3月下旬至4月下旬,友恩蚜小蜂种群密度高峰期则分别出现在2月下旬和4月下旬至5月中旬。另外友恩蚜小蜂对松突圆蚧的寄生率全年共出现2个高峰期,分别是2月下旬和4月下旬至6月上旬。结果表明,友恩蚜小蜂和寄主松突圆蚧在林间时空发生规律具有高度一致性。     

Irrigation as an important factor in species distribution models

Species distribution models (SDMs) that employ climatic variables are widely used to predict potential distribution of invasive species. However, climatic variables derived from climate datasets do not account for anthropogenic influences on microclimate. Irrigation is a major anthropogenic activity that influences microclimate conditions and alters the distribution of species in anthropogenic landuses. SDM-based studies appear to ignore the effects of irrigation on microclimatic conditions. This study incorporated irrigation as a correction to precipitation data, to improve the predictive capacity of SDM. As a case study, we examined a SDM of Wasmannia auropunctata, an invasive species that originates in South and Central America, which has invaded tropical and subtropical regions around the world. The potential distribution of W. auropunctata was predicted using Maxent. The model was built based on climatic variables and species records from non-irrigated sites in the native range and then projected on a global scale. Invasive species records were used to evaluate the performance of the model. Precipitation-related variables were modified to approximate actual water input in irrigated areas. Precipitation correction relied on an estimate of irrigation inputs. The model with irrigation correction performed better than the corresponding model without correction, on a global scale and when it was examined in five different geographical regions of the model. These results demonstrate the importance of irrigation correction for assessing the distribution of W. auropunctata in various geographical regions. Accounting for irrigation is expected to improve SDMs for a variety of species.