Predicting species distributions from museum and herbarium records using multiresponse models fitted with multivariate adaptive regression splines

Current circumstances — that the majority of species distribution records exist as presence‐only data (e.g. from museums and herbaria), and that there is an established need for predictions of species distributions — mean that scientists and conservation managers seek to develop robust methods for using these data. Such methods must, in particular, accommodate the difficulties caused by lack of reliable information about sites where species are absent. Here we test two approaches for overcoming these difficulties, analysing a range of data sets using the technique of multivariate adaptive regression splines (MARS). MARS is closely related to regression techniques such as generalized additive models (GAMs) that are commonly and successfully used in modelling species distributions, but has particular advantages in its analytical speed and the ease of transfer of analysis results to other computational environments such as a Geographic Information System. MARS also has the advantage that it can model multiple responses, meaning that it can combine information from a set of species to determine the dominant environmental drivers of variation in species composition. We use data from 226 species from six regions of the world, and demonstrate the use of MARS for distribution modelling using presence‐only data. We test whether (1) the type of data used to represent absence or background and (2) the signal from multiple species affect predictive performance, by evaluating predictions at completely independent sites where genuine presence–absence data were recorded. Models developed with absences inferred from the total set of presence‐only sites for a biological group, and using simultaneous analysis of multiple species to inform the choice of predictor variables, performed better than models in which species were analysed singly, or in which pseudo‐absences were drawn randomly from the study area. The methods are fast, relatively simple to understand, and useful for situations where data are limited. A tutorial is included.     

Can the cause of aggregation be inferred from species distributions?

Species distributions often show an aggregated pattern, which can be due to a number of endo- and exogenous factors. While autologistic models have been used for modelling such data with statistical rigour, little emphasis has been put on disentangling potential causes of aggregation. In this paper we ask whether it is possible to infer sources of aggregation in species distributions from a single set of occurrence data by comparing the performance of various autologistic models. We create simulated data sets, which show similar occupancy patterns, but differ in the process that causes the aggregation. We model the distribution of these data with various autologistic models, and show how the relative performance of the models is sensitive to the factor causing aggregation in the data. This information can be used when modelling real species data, where causes of aggregation are typically unknown. To illustrate, we use our approach to assess the potential causes of aggregation in data of seven bird species with contrasting statistical patterns. Our findings have important implications for conservation, as understanding the mechanisms that drive population fluctuations in space and time is critical for the development of effective management actions for long-term conservation.     

气候变化对马尾松潜在分布影响预估的多模型比较

物种分布模型被广泛应用于评估气候变化对物种分布的影响。随着计算机和统计学的发展, 模拟物种分布的模型层出不穷, 但对这些模型的相对表现知之甚少, 因此需要对其进行对比分析, 以便更可靠地评估气候变化的影响。该文采用3个比较新颖的组合集成学习(ensemble learning)模型(随机森林(random forest, RF)、广义助推法和NeuralEnsembles)、3个常规模型(广义线性模型、广义加法模型和分类回归树)、3个大气环流模型(global circulation model, GCM) (MIROC32_medres, JP; CCCMA_CGCM3, CA; BCCR-BCM2.0, NW)和一个气体排放情景(SRES_A2), 模拟分析了马尾松(Pinus massoniana)历史基准气候(1961-1990)和未来3个不同时期(2010-2039, 2020s; 2040-2069, 2050s; 2070-2099, 2080s)的潜在分布。基于环境阈值方法选择物种不发生区, 依据ClimateChina软件进行当前和未来气候数据的降尺度处理, 采用接收机工作特征曲线(receiver operator characteristic, ROC)下的面积(area under the curve, AUC)、Kappa值和真实技巧统计法(true skill statistic, TSS)以及马尾松种子区划范围来评价模型的预测精度。结果表明: 6个物种分布模型都具有较高的预测精度, 但组合集成学习模型的预测精度稍高于其他常规模型, 其中RF的预测精度最高。3个GCM和6个模型模拟条件下, 马尾松对气候变化的响应格局既有一致性也有异同性。一致性表现在: 随着时间的推移, 马尾松分布区将逐渐向北迁移, 未来潜在分布区的面积将逐渐增加; 异同性表现在: 在不同模型和不同气候情景下, 马尾松潜在分布区的迁移距离和面积变化幅度不同, 其中NW模式下预测的变化幅度小于CA和JP模式; RF模型预测的分布区迁移距离和面积变化幅度最大。随着时间的推移, 未来马尾松的18个潜在分布空间预测图(6个模型 × 3 GCM)之间的差异也逐渐增大, 其中空间不一致性地区主要集中发生在马尾松潜在分布区的北部和西部边缘地带。模型本身不同的构建原理以及GCM之间的差异是导致预测结果存在差异的主要原因。     

Modelling species distributions in Britain: a hierarchical integration of climate and land-cover data

A modelling framework for studying the combined effects of climate and land-cover changes on the distribution of species is presented. The model integrates land-cover data into a correlative bioclimatic model in a scale-dependent hierarchical manner, whereby Artificial Neural Networks are used to characterise species' climatic requirements at the European scale and land-cover requirements at the British scale. The model has been tested against an alternative non-hierarchical approach and has been applied to four plant species in Britain: Rhynchospora alba , Erica tetralix , Salix herbacea and Geranium sylvaticum . Predictive performance has been evaluated using Cohen's Kappa statistic and the area under the Receiver Operating Characteristic curve, and a novel approach to identifying thresholds of occurrence which utilises three levels of confidence has been applied. Results demonstrate reasonable to good predictive performance for each species, with the main patterns of distribution simulated at both 10 km and 1 km resolutions. The incorporation of land-cover data was found to significantly improve purely climate-driven predictions for R. alba and E. tetralix , enabling regions with suitable climate but unsuitable land-cover to be identified. The study thus provides an insight into the roles of climate and land-cover as determinants of species' distributions and it is demonstrated that the modelling approach presented can provide a useful framework for making predictions of distributions under scenarios of changing climate and land-cover type. The paper confirms the potential utility of multi-scale approaches for understanding environmental limitations to species' distributions, and demonstrates that the search for environmental correlates with species' distributions must be addressed at an appropriate spatial scale. Our study contributes to the mounting evidence that hierarchical schemes are characteristic of ecological systems.     

Effects of restricting environmental range of data to project current and future species distributions

We examine the consequences of restricting the range of environmental conditions over which niche-based models are developed to project potential future distributions of three selected European tree species to assess first, the importance of removing absences beyond species known distributions ("naughty noughts") and second the importance of capturing the full environmental range of species. We found that restricting the environmental range of data strongly influenced the estimation of response curves, especially towards upper and lower ends of environmental ranges. This induces changes in the probability values towards upper and lower environmental boundaries, leading to more conservative scenarios in terms of changes in distribution projections.
Using restricted data analogous to not capturing the fun species' environmental range, reduces strongly the combinations of environmental conditions under which the models are calibrated, and reduces the applicability of the models for predictive purposes. This may generate unpredictable effects on the tails of the species response curves, yielding spurious projections into the future provided that probability of occurrence is not set to zero outside the environmental limits of the species. Indeed, as the restricted data does not capture the whole of the response curve, projections of future species distributions based of ecological niche modelling may be only valid if niche models are able to approach the complete response curve of environmental predictors.     

An evaluation of methods for modelling species distributions

Aim Various statistical techniques have been used to model species probabilities of occurrence in response to environmental conditions. This paper provides a comprehensive assessment of methods and investigates whether errors in model predictions are associated to specific kinds of geographical and environmental distributions of species. Location Portugal, Western Europe. Methods Probabilities of occurrence for 44 species of amphibians and reptiles in Portugal were modelled using seven modelling techniques: Gower metric, Ecological Niche Factor Analysis, classification trees, neural networks, generalized linear models, generalized additive models and spatial interpolators. Generalized linear and additive models were constructed with and without a term accounting for spatial autocorrelation. Model performance was measured using two methods: sensitivity and Kappa index. Species were grouped according to their spatial (area of occupancy and extent of occurrence) and environmental (marginality and tolerance) distributions. Two‐way comparison tests were performed to detect significant interactions between models and species groups. Results Interaction between model and species groups was significant for both sensitivity and Kappa index. This indicates that model performance varied for species with different geographical and environmental distributions. Artificial neural networks performed generally better, immediately followed by generalized additive models including a covariate term for spatial autocorrelation. Non‐parametric methods were preferred to parametric approaches, especially when modelling distributions of species with a greater area of occupancy, a larger extent of occurrence, lower marginality and higher tolerance. Main conclusions This is a first attempt to relate performance of modelling techniques with species spatial and environmental distributions. Results indicate a strong relationship between model performance and the kinds of species distributions being modelled. Some methods performed generally better, but no method was superior in all circumstances. A suggestion is made that choice of the appropriate method should be contingent on the goals and kinds of distributions being modelled.     

运用聚类分析与Google Maps于大量物种出现记录之研究

物种出现记录包含博物馆动物标本、植物标本、生态调查与物种观察等资料。在台湾生物多样性信息机构(Taiwan Biodiversity Information Facility,TaiBIF)物种出现记录整合平台中,已整合台湾26个数据集,包含超过150万笔物种出现记录,其中约有85%的数据具有地理信息。我们利用数据库中所汇整的鲤科数据,包括11个数据集、超过8,800笔出现记录数据,利用网格式、切割式与密度式3种聚类分析算法分别绘制出不同的空间可视化结果,藉此解决大量物种出现记录于Google Maps上呈现效能与可视化不佳之问题。同时我们也探讨了3种聚类分析法之结果与鲤科的专家意见范围地图(expertopinion range maps)比对的差异。期望透过本研究可快速且有效地呈现物种分布资料,进而帮助研究者挖掘出大量数据所隐含的知识,并为生态保育提供重要参考。     

A taxonomic wish-list for community ecology

Community ecology seeks to explain the number and relative abundance of coexisting species. Four research frontiers in community ecology are closely tied to research in systematics and taxonomy: the statistics of species richness estimators, global patterns of biodiversity, the influence of global climate change on community structure, and phylogenetic influences on community structure. The most pressing needs for taxonomic information in community ecology research are usable taxonomic keys, current nomenclature, species occurrence records and resolved phylogenies. These products can best be obtained from Internet-based phylogenetic and taxonomic resources, but the lack of trained professional systematists and taxonomists threatens this effort. Community ecologists will benefit most directly from research in systematics and taxonomy by making better use of resources in museums and herbaria, and by actively seeking training, information and collaborations with taxonomic specialists.     

Presence-absence versus presence-only modelling methods for predicting bird habitat suitability

Habitat suitability models can be generated using methods requiring information on species presence or species presence and absence. Knowledge of the predictive performance of such methods becomes a critical issue to establish their optimal scope of application for mapping current species distributions under different constraints. Here, we use breeding bird atlas data in Catalonia as a working example and attempt to analyse the relative performance of two methods: the Ecological Niche factor Analysis (ENFA) using presence data only and Generalised Linear Models (GLM) using presence/absence data. Models were run on a set of forest species with similar habitat requirements, but with varying occurrence rates (prevalence) and niche positions (marginality). Our results support the idea that GLM predictions are more accurate than those obtained with ENFA. This was particularly true when species were using available habitats proportionally to their suitability, making absence data reliable and useful to enhance model calibration. Species marginality in niche space was also correlated to predictive accuracy, i.e. species with less restricted ecological requirements were modelled less accurately than species with more restricted requirements. This pattern was irrespective of the method employed. Models for wide‐ranging and tolerant species were more sensitive to absence data, suggesting that presence/absence methods may be particularly important for predicting distributions of this type of species. We conclude that modellers should consider that species ecological characteristics are critical in determining the accuracy of models and that it is difficult to predict generalist species distributions accurately and this is independent of the method used. Being based on distinct approaches regarding adjustment to data and data quality, habitat distribution modelling methods cover different application areas, making it difficult to identify one that should be universally applicable. Our results suggest however, that if absence data is available, methods using this information should be preferably used in most situations.     

On the generality of habitat distribution models: a case study of capercaillie in three Swiss regions

Predictive habitat distribution models are normally assumed to sacrifice generality for precision and reality. Nevertheless, such models are often applied to predict the distribution of a species outside the area for which the model has been calibrated.
We investigated how the geographic extent of the data used for calibration influenced the performance of habitat distribution models applied on independent data. We took a multi-scale logistic regression approach by varying the grain size to develop six habitat models for capercaillie Tetrao urogallus in Switzerland: three regional models, for the northern Pre-Alps, eastern Central Alps and Jura mountains, respectively, and three pooled models, each using data from two of the three regions. The six models were validated with data from the region(s) not used for model building. We used Cohen's Kappa and the area under the receiver operating characteristics curve as accuracy measures. The regional models performed well in the region where they had been calibrated, but poorly to moderately well in the other regions. The pooled models classified almost as well in their calibration regions as the corresponding regional models, but generally better when validated on data from the independent region. Hence, models built with data from single regions provide less certain predictions of species' distributions in other regions. We recommend building more general models using data pooled from several regions, when the aim is to predict species' distributions in independent regions.     

The dynamics of climate-induced range shifting; perspectives from simulation modelling

Predicted future climate change will alter species' distributions as they attempt to track the most suitable 'climate window'. Climate envelope models indicate the direction of likely range changes but do not incorporate population dynamics, therefore observed responses may differ greatly from these projections. We use simulation modelling to explore the consequences of a period of environmental change for a species structured across an environmental gradient. Results indicate that a species' range may lag behind its climate envelope and demonstrate that the rate of movement of a range can accelerate during a period of climate change. We conclude that the inclusion of both population dynamics and spatial environmental variability is vital to develop models that can both predict, and be used to manage, the impact of changing climate on species' biogeography.     

Extended statistical approaches to modelling spatial pattern in biodiversity in northeast New South Wales. I. Species-level modelling

Statistical modelling of biological survey data in relation to remotely mapped environmental variables is a powerful technique for making more effective use of sparse data in regional conservation planning. Application of such modelling to planning in the northeast New South Wales (NSW) region of Australia represents one of the most extensive and longest running case studies of this approach anywhere in the world. Since the early 1980s, statistical modelling has been used to extrapolate distributions of over 2300 species of plants and animals, and a wide variety of higher-level communities and assemblages. These modelled distributions have played a pivotal role in a series of major land-use planning processes, culminating in extensive additions to the region's protected area system. This paper provides an overview of the analytical methodology used to model distributions of individual species in northeast NSW, including approaches to: (1) developing a basic integrated statistical and geographical information system (GIS) framework to facilitate automated fitting and extrapolation of species models; (2) extending this basic approach to incorporate consideration of spatial autocorrelation, land-cover mapping and expert knowledge; and (3) evaluating the performance of species modelling, both in terms of predictive accuracy and in terms of the effectiveness with which such models function as general surrogates for biodiversity.     

Ecological niche modelling of the distribution of cold-water coral habitat using underwater remote sensing data

Despite a growing appreciation of the need to protect sensitive deep sea ecosystems such as cold-water corals, efforts to map the extent of their distribution are limited by their remoteness. Here we develop ecological niche models to predict the likely distributions of cold-water corals based on occurrence records and data describing environmental parameters (e.g. seafloor terrain attributes and oceanographic conditions). This study has used bathymetric data derived from ship-borne multibeam swath systems, species occurrence data from remotely operated vehicle video surveys and oceanographic parameters from hydrodynamic models to predict coral locations in regions where there is a paucity of direct observations. Predictions of the locations of the scleractinian coral, Lophelia pertusa are based primarily upon ecological niche modelling using a genetic algorithm. Its accuracy has been quantified at local (~ 25 km²) and regional scales (~ 4000 km²) along the Irish continental slope using a variety of error assessment techniques and a comparison with another ecological niche modelling technique. With appropriate choices of parameters and scales of analyses, ecological niche modelling has been effective in predicting the distributions of species at local and regional scales. Refinements of this approach have the potential to be particularly useful for ocean management given the need to manage areas of sensitive habitat where survey data are often limited.     

Habitat-based statistical models for predicting the spatial distribution of butterflies and day-flying moths in a fragmented landscape

1.  Most species' surveys and biodiversity inventories are limited by time and money. Therefore, it would be extremely useful to develop predictive models of animal distributions based on habitat, and to use these models to estimate species' densities and range sizes in poorly sampled regions.
2.  In this study, two sets of data were collected. The first set consisted of over 2000 butterfly transect counts, which were used to determine the relative density of each species in 16 major habitat types in a 35-km² area of fragmented landscape in north-west Wales. For the second set of data, the area was divided into 140 cells using a 500-m grid, and the extent of each habitat and the presence or absence of each butterfly and moth species was determined for each cell.
3.  Logistic regression was used to model the relationship between species' distribution and predicted density, based on habitat extent, in each grid square. The resultant models were used to predict butterfly distributions and occupancy at a range of spatial scales.
4.  Using a jack-knife procedure, our models successfully reclassified the presence or absence of species in a high percentage of grid squares (mean 83% agreement). There were highly significant relationships between the modelled probability of species occurring at regional and local scales and the number of grid squares occupied at those scales.
5.  We conclude that basic habitat data can be used to predict insect distributions and relative densities reasonably well within a fragmented landscape. It remains to be seen how accurate these predictions will be over a wider area.     

Dynamic distribution modelling: predicting the present from the past

Confidence in projections of the future distributions of species requires demonstration that recently-observed changes could have been predicted adequately. Here we use a dynamic model framework to demonstrate that recently-observed changes at the expanding northern boundaries of three British butterfly species can be predicted with good accuracy. Previous work established that the distributions of the study species currently lag behind climate change, and so we presumed that climate is not currently a major constraint at the northern range margins of our study species. We predicted 1970–2000 distribution changes using a colonisation model, MIGRATE, superimposed on a high-resolution map of habitat availability. Thirty-year rates and patterns of distribution change could be accurately predicted for each species (κ goodness-of-fit of models >0.64 for all three species, corresponding to >83% of grid cells correctly assigned), using a combination of individual species traits, species-specific habitat associations and distance-dependent dispersal. Sensitivity analyses showed that population productivity was the most important determinant of the rate of distribution expansion (variation in dispersal rate was not studied because the species are thought to be similar in dispersal capacity), and that each species' distribution prior to expansion was critical in determining the spatial pattern of the current distribution. In future, modelling approaches that combine climate suitability and spatially-explicit population models, incorporating demographic variables and habitat availability, are likely to be valuable tools in projecting species' responses to climatic change and hence in anticipating management to facilitate species' dispersal and persistence.     

Where is positional uncertainty a problem for species distribution modelling?

Species data held in museum and herbaria, survey data and opportunistically observed data are a substantial information resource. A key challenge in using these data is the uncertainty about where an observation is located. This is important when the data are used for species distribution modelling (SDM), because the coordinates are used to extract the environmental variables and thus, positional error may lead to inaccurate estimation of the species–environment relationship. The magnitude of this effect is related to the level of spatial autocorrelation in the environmental variables. Using local spatial association can be relevant because it can lead to the identification of the specific occurrence records that cause the largest drop in SDM accuracy. Therefore, in this study, we tested whether the SDM predictions are more affected by positional uncertainty originating from locations that have lower local spatial association in their predictors. We performed this experiment for Spain and the Netherlands, using simulated datasets derived from well known species distribution models (SDMs). We used the K statistic to quantify the local spatial association in the predictors at each species occurrence location. A probabilistic approach using Monte Carlo simulations was employed to introduce the error in the species locations. The results revealed that positional uncertainty in species occurrence data at locations with low local spatial association in predictors reduced the prediction accuracy of the SDMs. We propose that local spatial association is a way to identify the species occurrence records that require treatment for positional uncertainty. We also developed and present a tool in the R environment to target observations that are likely to create error in the output from SDMs as a result of positional uncertainty.     

Individualistic vs community modelling of species distributions under climate change

Studies investigating the consequences of future climate changes on species distributions usually start with the assumption that species respond to climate changes in an individualistic fashion. This assumption has led researchers to use bioclimate envelope models that use present climate-range relationships to characterize species' limits of tolerance to climate, and then apply climate-change scenarios to enable projections of altered species distributions. However, there are techniques that combine climate variables together with information on the composition of assemblages to enable projections that are expected to mimic community dynamics. Here, we compare, for the first time, the performance of GLM (generalized linear model) and CQO (canonical quadratic ordination; a type of community-based GLM) for projecting distributions of species under climate change scenarios. We found that projections from these two methods varied both in terms of accuracy (GLM providing generally more accurate projections than CQO) and in the broad diversity patterns yielded (higher species richness values projected with CQO). Model outputs were also affected by species-specific traits, such as species range size and species geographical positions, supporting the view that methods are sensitive to different degrees of equilibrium of species distributions with climate. This study reveals differences in projections between individual- and community-based approaches that require further scrutiny, but it does not find support for unsupervised use community-based models for investigating climate change impacts on species distributions. Reasons for this lack of support are discussed.     

Tracking biological invasions in space and time: elucidating the invasive history of the green alga Codium fragile using old DNA

With the advent of 'ancient DNA' studies on preserved material of extant and extinct species, museums and herbaria now represent an important although still underutilized resource in molecular ecology. The ability to obtain sequence data from archived specimens can reveal the recent history of cryptic species and introductions. We have analysed extant and herbarium samples of the highly invasive green alga Codium fragile , many over 100 years old, to identify cryptic accessions of the invasive strain known as C. fragile ssp. tomentosoides , which can be identified by a unique haplotype. Molecular characterization of specimens previously identified as native in various regions shows that the invasive tomentosoides strain has been colonizing new habitats across the world for longer than records indicate, in some cases nearly 100 years before it was noticed. It can now be found in the ranges of all the other native haplotypes detected, several of which correspond to recognized subspecies. Within regions in the southern hemisphere there was a greater diversity of haplotypes than in the northern hemisphere, probably as a result of dispersal by the Antarctic Circumpolar Current. The findings of this study highlight the importance of herbaria in preserving contemporaneous records of invasions as they occur, especially when invasive taxa are cryptic.     

Pleistocene fragmentation of Amazon species' ranges

Historical patterns of connection and isolation of the impressive biological diversity of the Amazon Basin have been the subject of extensive debate, based on evidence drawn from distributional patterns of endemic species, vegetation histories from palynological studies, and geological studies. We develop species-specific ecological niche models based on current occurrence patterns of 17 species of birds and woody plants, which we project onto modelled Pleistocene (Last Glacial Maximum) climatic patterns to reconstruct past potential distributions of each species. Forest species' distributions showed fragmentation at Last Glacial Maximum and these fragments were coincident spatially, whereas savanna species showed no clear trends. Our results suggest that past climate changes fragmented forest species' ranges within a matrix of uncertain composition.     

The contribution of small collections to species distribution modelling: A case study from Fuireneae (Cyperaceae)

The recent and rapid digitization of biodiversity data from natural history collection (NHC) archives has enriched collections based data repositories; this data continues to inform studies of species' geographic distributions. Here we investigate the relative impact of plant data from small natural history collections (collections with < 100,000 specimens) on species distributional models in an effort to document the potential of data from small NHCs to contribute to and inform biodiversity research. We modelled suitable habitat of five test case species from Fuireneae (Cyperaceae) in the United States using specimen records available via the Global Biodiversity Information Facility and that of data ready to mobilize from two regional small herbaria. Data were partitioned into three datasets based on their source: 1) collections-based records from large NHCs accessed GBIF, 2) collections-based records from small NHCs accessed from GBIF, and 3) collections-based records from two small regional herbaria not yet mobilized to GBIF. We extracted and evaluated the ecological niche represented for each of the three datasets by applying dataset occurrences to 14 environmental factors, and we modelled habitat suitability using Maxent to compare the represented distribution of the environmental values among the datasets. Our analyses indicate that the data from small NHCs contributed unique information in both geographic and environmental space. When data from small collections were combined with data from large collections, species models of the ecological niche resulted in more refined predictions of habitat suitability, indicating that small collections can contribute unique occurrence data which enhance species distribution models by bridging geographic collection gaps and shifting modelled predictions of suitable habitat. Inclusion of specimen records from small collections in ongoing digitization efforts is essential for generating informed models of a species' niche and distribution.