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.     

Stream diatoms exhibit weak niche conservation along global environmental and climatic gradients

Niche conservatism (NC) describes the scenario in which species retain similar characteristics or traits over time and space, and thus has potentially important implications for understanding their biogeographic distributions. Evidence consistent with NC includes similar niche properties across geographically distant regions. We investigated whether NC was evident in stream diatom morphospecies by modeling species responses to environmental and climatic variables in a set of calibration sites (from the US) and then evaluated the models with test sets (from France, Finland, New Zealand, Antilles and La Réunion). We also examined whether diatom species showed congruency in environmental niche optima and niche breadths between the study regions, and whether species occupancy and functional traits influenced the observed patterns. We used boosted regression tree models with local environmental variables and climatic variables as predictors. We detected low NC in both environmental and climate models and a lack of consistent differences in NC between widely distributed and regionally rare species and among functional groups. For all species, diatom environmental and climatic optima varied clearly between the regions but showed some positive relationships especially for pH and total phosphorus. Diatom niche breadths were only weakly correlated between the US and the other regions. We demonstrated that diatoms showed overall relatively little NC globally, and NC was especially low for climatic variables. Collectively, these findings suggest that there may exist locally adapted lineages within the diatom morphospecies or diatoms possess some adaptation potential for differences in temperature. We argue that in diatoms, environmental and especially climate models may not be transferrable in space globally but need regional diatom data for calibration because species niches seem to differ among geographical regions.     

Predicting spatial patterns of plant species richness: a comparison of direct macroecological and species stacking modelling approaches

Aim This study compares the direct, macroecological approach (MEM) for modelling species richness (SR) with the more recent approach of stacking predictions from individual species distributions (S‐SDM). We implemented both approaches on the same dataset and discuss their respective theoretical assumptions, strengths and drawbacks. We also tested how both approaches performed in reproducing observed patterns of SR along an elevational gradient. Location Two study areas in the Alps of Switzerland. Methods We implemented MEM by relating the species counts to environmental predictors with statistical models, assuming a Poisson distribution. S‐SDM was implemented by modelling each species distribution individually and then stacking the obtained prediction maps in three different ways – summing binary predictions, summing random draws of binomial trials and summing predicted probabilities – to obtain a final species count. Results The direct MEM approach yields nearly unbiased predictions centred around the observed mean values, but with a lower correlation between predictions and observations, than that achieved by the S‐SDM approaches. This method also cannot provide any information on species identity and, thus, community composition. It does, however, accurately reproduce the hump‐shaped pattern of SR observed along the elevational gradient. The S‐SDM approach summing binary maps can predict individual species and thus communities, but tends to overpredict SR. The two other S‐SDM approaches – the summed binomial trials based on predicted probabilities and summed predicted probabilities – do not overpredict richness, but they predict many competing end points of assembly or they lose the individual species predictions, respectively. Furthermore, all S‐SDM approaches fail to appropriately reproduce the observed hump‐shaped patterns of SR along the elevational gradient. Main conclusions Macroecological approach and S‐SDM have complementary strengths. We suggest that both could be used in combination to obtain better SR predictions by following the suggestion of constraining S‐SDM by MEM predictions.     

How to deal with ground truthing affected by human‐induced habitat change?: Identifying high‐quality habitats for the Critically Endangered Red Siskin

Species distribution models (SDM) can be valuable for identifying key habitats for conservation management of threatened taxa, but anthropogenic habitat change can undermine SDM accuracy. We used data for the Red Siskin (Spinus cucullatus), a critically endangered bird and ground truthing to examine anthropogenic habitat change as a source of SDM inaccuracy. We aimed to estimate: (1) the Red Siskin's historic distribution in Venezuela; (2) the portion of this historic distribution lost to vegetation degradation; and (3) the location of key habitats or areas with both, a high probability of historic occurrence and a low probability of vegetation degradation. We ground‐truthed 191 locations and used expert opinion as well as landscape characteristics to classify species' habitat suitability as excellent, good, acceptable, or poor. We fit a Random Forest model (RF) and Enhanced Vegetation Index (EVI) time series to evaluate the accuracy and precision of the expert categorization of habitat suitability. We estimated the probability of historic occurrence by fitting a MaxLike model using 88 presence records (1960–2013) and data on forest cover and aridity index. Of the entire study area, 23% (20,696 km²) had a historic probability of Red Siskin occurrence over 0.743. Furthermore, 85% of ground‐truthed locations had substantial reductions in mean EVI, resulting in key habitats totaling just 976 km², in small blocks in the western and central regions. Decline in Area of Occupancy over 15 years was between 40% and 95%, corresponding to an extinction risk category between Vulnerable and Critically Endangered. Relating key habitats with other landscape features revealed significant risks and opportunities for proposed conservation interventions, including the fact that ongoing vegetation degradation could limit the establishment of reintroduced populations in eastern areas, while the conservation of remaining key habitats on private lands could be improved with biodiversity‐friendly agri‐ and silviculture programs.     

Is more data always better? A simulation study of benefits and limitations of integrated distribution models

Species distribution models are popular and widely applied ecological tools. Recent increases in data availability have led to opportunities and challenges for species distribution modelling. Each data source has different qualities, determined by how it was collected. As several data sources can inform on a single species, ecologists have often analysed just one of the data sources, but this loses information, as some data sources are discarded. Integrated distribution models (IDMs) were developed to enable inclusion of multiple datasets in a single model, whilst accounting for different data collection protocols. This is advantageous because it allows efficient use of all data available, can improve estimation and account for biases in data collection. What is not yet known is when integrating different data sources does not bring advantages. Here, for the first time, we explore the potential limits of IDMs using a simulation study integrating a spatially biased, opportunistic, presence-only dataset with a structured, presence–absence dataset. We explore four scenarios based on real ecological problems; small sample sizes, low levels of detection probability, correlations between covariates and a lack of knowledge of the drivers of bias in data collection. For each scenario we ask; do we see improvements in parameter estimation or the accuracy of spatial pattern prediction in the IDM versus modelling either data source alone? We found integration alone was unable to correct for spatial bias in presence-only data. Including a covariate to explain bias or adding a flexible spatial term improved IDM performance beyond single dataset models, with the models including a flexible spatial term producing the most accurate and robust estimates. Increasing the sample size of presence–absence data and having no correlated covariates also improved estimation. These results demonstrate under which conditions integrated models provide benefits over modelling single data sources.     

Climatic Control on Forests and Tree Species Distribution in the Forest Region of Northeast China

North-east （NE） China covers considerable climatic gradients and all major forests types of NE Asia. in the present study, 10 major forest types across the forest region of NE China were sampled to Investigate forest distribution in relation to climate. Canonical correspondence analysis （CCA） revealed that growing season precipitation and energy availability were primary climatic factors for the overall forest pattern of NE China, accounting for 66% of the explanatory power of CCA. Conversely, annual precipitation and winter coldness had minor effects. Generalized additive models revealed that tree species responded to climatic gradients differently and showed three types of response curve： （i） monotonous decline; （ii） monotonous Increase; and （iii） a unimodai pattern. Furthermore, tree species showed remarkable differences in limiting climatic factors for their distribution. The power of climate in explaining species distribution declined significantly with decreasing species dominance, suggesting that the distribution of dominant species was primarily controlled by climate, whereas that of subordinate species was more affected by competition from other species.     

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。     

A problem with variable selection in a comparison of correlative and process‐based species distribution models: Comments on Higgins et al., 2020

Comments are presented on an article published in October 2020 in Ecology and Evolution (“Predictive ability of a process‐based versus a correlative species distribution model”) by Higgins et al. This analyzed natural distributions of Australian eucalypt and acacia species and assessed the adventive range of selected species outside Australia. Unfortunately, inappropriate variables were used with the MaxEnt species distribution model outside Australia, so that large climatically suitable areas in the Northern Hemisphere were not identified. Examples from a previous analysis and from the use of the freely available spatial portal of the Atlas of Living Australia are provided to illustrate how the problem can be overcome. The comparison of methods described in the Higgins et al. paper is worthwhile, and it is hoped that the authors will be able to repeat their analyses using appropriate variables with the correlative model.     

The future representativeness of Madagascar's protected area network in the face of climate change

With many species predicted to respond to a changing climate by shifting their distribution to climatically suitable areas, the effectiveness of static protected areas (PAs) is in question. The Madagascan PA network area has quadrupled over the past 15 years, and, although conservation planning techniques were employed to prioritise suitable areas for protection during this process, climate change impacts were not considered. We make use of species distribution models for 750 Madagascan vertebrate species to assess the potential impacts of climate change on (1) species richness across Madagascar, (2) species gain, loss and turnover in Madagascar's PAs and (3) PA network representativeness. Results indicate that Madagascar is predicted to experience substantial shifts in species richness, with most PAs predicted to experience high rates of species turnover. Provided there are no barriers to species movements, the representativeness of the current PA network will remain high for the species that are predicted to survive changes in climate by 2070, suggesting that little benefit will be gained from establishing new PAs. However, this rests on the assumption of mobility through areas currently characterised by fragmentation and anthropogenic activity, something that will require considerable expansion in conservation efforts in order to achieve.     

中国三种常见蒿属植物潜在地理分布及其主导气候因子

裂叶蒿(Artemisia tanacetifolia)、大籽蒿(Artemisia sieversiana)和艾(Artemisia argyi)是我国常见的蒿属(Artemisia)植物,其分布区域遍布全国.本文利用MaxEnt模型预测3种蒿属植物在当前气候条件以及未来两种气候情景下的潜在分布区.采用受试者工作特征...     

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.     

Refugee species: which historic baseline should inform conservation planning?

Understanding species' historical ranges can provide important information for conservation planning in the face of environmental change. Cromsigt et al. (this issue) comment on our recent European bison (Bison bonasus) range reconstruction, suggesting that bison were already 8000 years ago a refugee species (i.e. restricted to marginal habitat due to past human pressure) and that species distribution models (SDM) are generally of limited use for refugee species conservation. While we welcome this discussion, we find no evidence for the claim that human pressure prior to 8000 BP determined where bison occurred. More importantly, as human pressure is generally high and increasing, attempts to restore species across their former range may fail where the factors that relegated species into refugee status are still at play or where their optimal habitat has vanished. Identifying areas where human pressure is low and where refugee species have persisted over the last millennia is crucial, and SDM based on historical data are important for doing so. Refugee species suffer from the shifting baseline syndrome, but careful reality checks are needed and all available data should be considered before determining the baseline that should inform conservation planning.     

Species distribution models predict temporal but not spatial variation in forest growth

Bioclimate envelope models have been widely used to illustrate the discrepancy between current species distributions and their potential habitat under climate change. However, the realism and correct interpretation of such projections has been the subject of considerable discussion. Here, we investigate whether climate suitability predictions correlate to tree growth, measured in permanent inventory plots and inferred from tree‐ring records. We use the ensemble classifier RandomForest and species occurrence data from ~200,000 inventory plots to build species distribution models for four important European forestry species: Norway spruce, Scots pine, European beech, and pedunculate oak. We then correlate climate‐based habitat suitability with volume measurements from ~50‐year‐old stands, available from ~11,000 inventory plots. Secondly, habitat projections based on annual historical climate are compared with ring width from ~300 tree‐ring chronologies. Our working hypothesis is that habitat suitability projections from species distribution models should to some degree be associated with temporal or spatial variation in these growth records. We find that the habitat projections are uncorrelated with spatial growth records (inventory plot data), but they do predict interannual variation in tree‐ring width, with an average correlation of .22. Correlation coefficients for individual chronologies range from values as high as .82 or as low as ?.31. We conclude that tree responses to projected climate change are highly site‐specific and that local suitability of a species for reforestation is difficult to predict. That said, projected increase or decrease in climatic suitability may be interpreted as an average expectation of increased or reduced growth over larger geographic scales.