Equivalence of citizen science and scientific data for modelling species distribution of birds from a tropical savanna

The Wallacean deficit continues to be a challenge to species distribution modelling. Although some authors have suggested that data collected by citizen scientists can be relevant for a better understanding of biodiversity, to our knowledge, no work has quantitatively tested the equivalence between scientific and citizen science data. Here, we investigate the hypothesis that data collected by citizen scientists can be equivalent to data collected by professional scientists when generating species spatial distribution models. For 42 bird species in the Cerrado region we generated and compared species distribution models based on three data sources: (1) scientific data, (2) citizen science data and (3) sample size corrected citizen science data. To test our hypothesis, we compared the equivalence of these datasets. We rejected the hypothesis of equivalence for about one-third (38%) of the evaluated species, revealing that, for most of the species considered, the models generated were equivalent irrespective of the data set used. The distances between centroids of the models that were equivalent were on average smaller than the distances between non-equivalent models. Also, the direction of change in the models showed no pattern, with no trend towards more populated regions. Our results show that the use of data collected by citizen scientists can be an ally in filling the Wallacean deficit gap. In fact, the lack of use of this wide range of data collected by citizen scientists seems to be an unjustified caution. We indicate the potential of using citizen science data for modelling the distribution of species, mainly due to the large set of data collected, which is impracticable for scientists alone to collect. Conservation measures will be favoured by the union of professional and amateur data, aiming for a better understanding of species distribution and, consequently, biodiversity conservation.     

A standard protocol for reporting species distribution models

Species distribution models (SDMs) constitute the most common class of models across ecology, evolution and conservation. The advent of ready-to-use software packages and increasing availability of digital geoinformation have considerably assisted the application of SDMs in the past decade, greatly enabling their broader use for informing conservation and management, and for quantifying impacts from global change. However, models must be fit for purpose, with all important aspects of their development and applications properly considered. Despite the widespread use of SDMs, standardisation and documentation of modelling protocols remain limited, which makes it hard to assess whether development steps are appropriate for end use. To address these issues, we propose a standard protocol for reporting SDMs, with an emphasis on describing how a study's objective is achieved through a series of modeling decisions. We call this the ODMAP (Overview, Data, Model, Assessment and Prediction) protocol, as its components reflect the main steps involved in building SDMs and other empirically-based biodiversity models. The ODMAP protocol serves two main purposes. First, it provides a checklist for authors, detailing key steps for model building and analyses, and thus represents a quick guide and generic workflow for modern SDMs. Second, it introduces a structured format for documenting and communicating the models, ensuring transparency and reproducibility, facilitating peer review and expert evaluation of model quality, as well as meta-analyses. We detail all elements of ODMAP, and explain how it can be used for different model objectives and applications, and how it complements efforts to store associated metadata and define modelling standards. We illustrate its utility by revisiting nine previously published case studies, and provide an interactive web-based application to facilitate its use. We plan to advance ODMAP by encouraging its further refinement and adoption by the scientific community.     

The difficulty of using species distribution modelling for the conservation of refugee species – the example of European bison

Refugee species have been confined to suboptimal habitat through historic anthropogenic factors. If this is unknown, management might actively conserve these species in suboptimal habitat assuming it represents optimal habitat. Similarly, species distribution modelling (SDM) might misguide conservation management of refugee species by only using presence data from suboptimal habitats. We illustrate this by commenting on a recent SDM for European bison that reconstructed the historic distribution of the species. We challenge the interpretation of this model by suggesting an alternative historic biogeography based on the refugee species concept. We argue that, in the case of refugee species, historic reconstructions using SDM cannot be used as a template for conservation management. Rather, experimental re‐introduction programmes should provide us with population performance and life history data from a range of suboptimal to optimal habitats. Such data could be used in mechanistic niche modelling to predict potential distribution of refugee species.     

虚拟物种的基本原理及其在物种分布模型评估中的应用

物种分布模型是建立在物种出现或缺失数据的基础上,但可获得的真实分布数据存在着各种各样的缺点(如:物种识别错误、坐标错误、抽样偏差、数据缺失等),影响着物种分布模型的预测性能、稳定性及应用,因此使用物种真实分布数据评估物种分布模型将带来很大的不确定性。为避免这种不确定性,越来越多的研究使用虚拟物种来评价物种分布模型的性能,评估新方法的优劣。虚拟物种是一种建立在真实(或虚拟)地理信息系统下人工生命,是简化和抽象的物种,它通过模拟物种对环境变量的响应关系,评估物种在不同环境变量下的出现概率,人为地给出虚拟的物种分布数据。虚拟物种具有数据容易获得、数据质量可控、避免过度模拟等优势,目前它被广泛用于评估物种特性、抽样偏差、地理信息、出现/缺失标准等对物种分布模型性能的影响。虚拟物种是大尺度研究中不可或缺的重要工具,有利于解决真实数据未能解决的科学问题。常用的构成算法有求和法、求积法和综合法,但这些方法可能存在补偿效应,扩大了物种的分布范围。考虑到虚拟物种的不足,提出了未来虚拟物种可能的发展方向(避免过度脱离真实,完善虚拟物种的构成算法,构建虚拟的模式生物、群落及生态系统等)。为帮助研究者快速构建虚拟物种,基于R环境开发了一个虚拟物种构成软件包(SDMvspecies)。虚拟物种可以与真实物种相结合,通过改进模型的构成方法,有利于解决一些真实数据未能解决的问题;虚拟物种的应用也将导致一些新理论的产生,有利于更好地理解生态学原理。     

Incorporating intraspecific variation into species distribution models improves distribution predictions,but cannot predict species traits for a wide-spread plant species

The most common approach to predicting how species ranges and ecological functions will shift with climate change is to construct correlative species distribution models (SDMs). These models use a species’ climatic distribution to determine currently suitable areas for the species and project its potential distribution under future climate scenarios. A core, rarely tested, assumption of SDMs is that all populations will respond equivalently to climate. Few studies have examined this assumption, and those that have rarely dissect the reasons for intraspecific differences. Focusing on the arctic-alpine cushion plant Silene acaulis, we compared predictive accuracy from SDMs constructed using the species’ full global distribution with composite predictions from separate SDMs constructed using subpopulations defined either by genetic or habitat differences. This is one of the first studies to compare multiple ways of constructing intraspecific-level SDMs with a species-level SDM. We also examine the contested relationship between relative probability of occurrence and species performance or ecological function, testing if SDM output can predict individual performance (plant size) and biotic interactions (facilitation). We found that both genetic- and habitat-informed SDMs are considerably more accurate than a species-level SDM, and that the genetic model substantially differs from and outperforms the habitat model. While SDMs have been used to infer population performance and possibly even biotic interactions, in our system these relationships were extremely weak. Our results indicate that individual subpopulations may respond differently to climate, although we discuss and explore several alternative explanations for the superior performance of intraspecific-level SDMs. We emphasize the need to carefully examine how to best define intraspecific-level SDMs as well as how potential genetic, environmental, or sampling variation within species ranges can critically affect SDM predictions. We urge caution in inferring population performance or biotic interactions from SDM predictions, as these often-assumed relationships are not supported in our study.     

中国植物分布模拟研究现状

在过去的20年里, 物种分布模型已广泛应用于动植物地理分布的模拟研究。该文以植物物种分布模拟为例, 利用中国知网、维普网以及Web of Science文献数据库的检索与统计, 分析了2000-2018年间, 中国研究人员利用各种物种分布模型对植物物种分布模拟研究的发文量、模拟模型、物种类型、数据来源、研究目的等信息。最终共收集到366篇有效文献, 分析表明2011年以来中国的物种分布模型应用发展迅速, 且以最近5年最为迅猛, 在生态学、中草药业、农业和林业等行业部门应用广泛。在使用的33种模型中, 应用最广的为最大熵模型(MaxEnt)。有一半研究的环境数据仅包含气候数据, 另一半研究不仅包含气候数据还包括地形与土壤等数据; 环境及物种数据的来源多样, 国际及本土数据库均得到使用。模拟涉及有明确清单的562个植物种, 既有木本植物(52.7%), 也有草本植物(41.8%), 其中中草药、果树、园林植物、农作物等占比较高。研究目的主要集中在过去、现在和未来气候变化对植物种分布的影响及预测, 以及物种分布评估与生物多样性评价(包括入侵植物风险评估)两大方面。预测物种潜在分布范围与气候变化影响等基础研究, 与模拟物种适生区与推广种植等应用研究并重, 物种分布模型在生态学与农业、林业和中草药业等多学科、多行业开展多种应用, 多物种、多模型和多来源数据共同参与模拟与比较, 开发新的机理性物种分布模型, 拓展新的物种分布模拟应用领域, 是今后研究的重点发展方向。     

Citizen science can complement professional invasive plant surveys and improve estimates of suitable habitat

Aim

Citizen science is a cost-effective potential source of invasive species occurrence data. However, data quality issues due to unstructured sampling approaches may discourage the use of these observations by science and conservation professionals. This study explored the utility of low-structure iNaturalist citizen science data in invasive plant monitoring. We first examined the prevalence of invasive taxa in iNaturalist plant observations and sampling biases associated with these data. Using four invasive species as examples, we then compared iNaturalist and professional agency observations and used the two datasets to model suitable habitat for each species.

Location

Hawai'i, USA.

Methods

To estimate the prevalence of invasive plant data, we compared the number of species and observations recorded in iNaturalist to botanical checklists for Hawai'i. Sampling bias was quantified along gradients of site accessibility, protective status and vegetation disturbance using a bias index. Habitat suitability for four invasive species was modelled in Maxent, using observations from iNaturalist, professional agencies and stratified subsets of iNaturalist data.

Results

iNaturalist plant observations were biased towards invasive species, which were frequently recorded in areas with higher road/trail density and vegetation disturbance. Professional observations of four example invasive species tended to occur in less accessible, native-dominated sites. Habitat suitability models based on iNaturalist versus professional data showed moderate overlap and different distributions of suitable habitat across vegetation disturbance classes. Stratifying iNaturalist observations had little effect on how suitable habitat was distributed for the species modelled in this study.

Main Conclusions

Opportunistic iNaturalist observations have the potential to complement and expand professional invasive plant monitoring, which we found was often affected by inverse sampling biases. Invasive species represented a high proportion of iNaturalist plant observations, and were recorded in environments that were not captured by professional surveys. Combining the datasets thus led to more comprehensive estimates of suitable habitat.     

Correcting for bias in distribution modelling for rare species using citizen science data

Aim

To improve the accuracy of inferences on habitat associations and distribution patterns of rare species by combining machine‐learning, spatial filtering and resampling to address class imbalance and spatial bias of large volumes of citizen science data.

Innovation

Modelling rare species’ distributions is a pressing challenge for conservation and applied research. Often, a large number of surveys are required before enough detections occur to model distributions of rare species accurately, resulting in a data set with a high proportion of non‐detections (i.e. class imbalance). Citizen science data can provide a cost‐effective source of surveys but likely suffer from class imbalance. Citizen science data also suffer from spatial bias, likely from preferential sampling. To correct for class imbalance and spatial bias, we used spatial filtering to under‐sample the majority class (non‐detection) while maintaining all of the limited information from the minority class (detection). We investigated the use of spatial under‐sampling with randomForest models and compared it to common approaches used for imbalanced data, the synthetic minority oversampling technique (SMOTE), weighted random forest and balanced random forest models. Model accuracy was assessed using kappa, Brier score and AUC. We demonstrate the method by evaluating habitat associations and seasonal distribution patterns using citizen science data for a rare species, the tricoloured blackbird (Agelaius tricolor).

Main Conclusions

Spatial under‐sampling increased the accuracy of each model and outperformed the approach typically used to direct under‐sampling in the SMOTE algorithm. Our approach is the first to characterize winter distribution and movement of tricoloured blackbirds. Our results show that tricoloured blackbirds are positively associated with grassland, pasture and wetland habitats, and negatively associated with high elevations or evergreen forests during both winter and breeding seasons. The seasonal differences in distribution indicate that individuals move to the coast during the winter, as suggested by historical accounts.

     

Spatial analysis improves species distribution modelling during range expansion

Species distribution models (SDMs) assume equilibrium between species' distribution and the environment. However, this assumption can be violated under restricted dispersal and spatially autocorrelated environmental conditions. Here we used a model to simulate species' ranges expansion under two non-equilibrium scenarios, evaluating the performance of SDM coupled with spatial eigenvector mapping. The highest fit is for the models that include space, although the relative importance of spatial variables during the range expansion differs in the two scenarios. Incorporating space to the models was important only under colonization-lag non-equilibrium, under the expected scenario. Thus, mechanisms that generate range cohesion and determine species' distribution under climate changes can be captured by spatial modelling, with advantages compared with other techniques and in line with recent claims that SDMs have to account for more complex dynamic scenarios.     

Predicting habitat distribution and frequency from plant species co-occurrence data

Aim Species frequency data have been widely used in nature conservation to aid management decisions. To determine species frequencies, information on habitat occurrence is important: a species with a low frequency is not necessarily rare if it occupies all suitable habitats. Often, information on habitat distribution is available for small geographic areas only. We aim to predict grid‐based habitat occurrence from grid‐based plant species distribution data in a meso‐scale analysis. Location The study was carried out over two spatial extents: Germany and Bavaria. Methods Two simple models were set up to examine the number of characteristic plant species needed per grid cell to predict the occurrence of four selected habitats (species data from FlorKart, http://www.floraweb.de ). Both models were calibrated in Bavaria using available information on habitat distribution, validated for other federal states, and applied to Germany. First, a spatially explicit regression model (generalized linear model (GLM) with assumed binomial error distribution of response variable) was obtained. Second, a spatially independent optimization model was derived that estimated species numbers without using spatial information on habitat distribution. Finally, an additional uncalibrated model was derived that calculated the frequencies of 24 habitats. It was validated using NATURA2000 habitat maps. Results Using the Bavarian models it was possible to predict habitat distribution and frequency from the co‐occurrence of habitat‐specific species per grid cell. As the model validations for other German federal states were successful, the models were applied to all of Germany, and habitat distribution and frequencies could be retrieved for the national scale on the basis of habitat‐specific species co‐occurrences per grid cell. Using the third, uncalibrated model, which includes species distribution data only, it was possible to predict the frequencies of 24 habitats based on the co‐occurrence of 24% of formation‐specific species per grid cell. Predicted habitat frequencies deduced from this third model were strongly related to frequencies of NATURA2000 habitat maps. Main conclusions It was concluded that it is possible to deduce habitat distributions and frequencies from the co‐occurrence of habitat‐specific species. For areas partly covered by habitat mappings, calibrated models can be developed and extrapolated to larger areas. If information on habitat distribution is completely lacking, uncalibrated models can still be applied, providing coarse information on habitat frequencies. Predicted habitat distributions and frequencies can be used as a tool in nature conservation, for example as correction factors for species frequencies, as long as the species of interest is not included in the model set‐up.     

Predicting risk of invasion in a Mediterranean island using niche modelling and valuable biota

Invasion by alien species is nowadays considered as one of the major threats to biodiversity. Thus, the identification of the areas exposed to a greater risk of invasion represents a priority for management purpose, especially in presence of habitats worthy of conservation. This paper aims to propose a method to produce a map of risk of invasion, merging together the threat of invasion by invasive plants and the distribution of habitats with high conservation value, on the case study of the Island of Elba (Tuscan Archipelago). We modelled the potential distribution of six particularly harmful invasive plants and merged these distributions into a map of threat of invasion. This map was overlapped to the map of density of Natura2000 habitats, finally obtaining a map of risk of invasion. According to our analyses, the potential distribution of the invasive species resulted highly influenced by human-related factors. The habitats more at risk are those closer to streets and anthropic habitats, which are more likely to be colonized by the invasive species we studied. We identified some rare habitats which are strongly endangered, highlighting that around 20% of the surface of the Island is exposed to some level of risk of invasion.     

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.     

How citizen science could improve species distribution models and their independent assessment

1. Species distribution models (SDM) have been increasingly developed in recent years, but their validity is questioned. Their assessment can be improved by the use of independent data, but this can be difficult to obtain and prohibitive to collect. Standardized data from citizen science may be used to establish external evaluation datasets and to improve SDM validation and applicability.
2. We used opportunistic presence‐only data along with presence–absence data from a standardized citizen science program to establish and assess habitat suitability maps for 9 species of amphibian in western France. We assessed Generalized Additive and Random Forest Models’ performance by (1) cross‐validation using 30% of the opportunistic dataset used to calibrate the model or (2) external validation using different independent datasets derived from citizen science monitoring. We tested the effects of applying different combinations of filters to the citizen data and of complementing it with additional standardized fieldwork.
3. Cross‐validation with an internal evaluation dataset resulted in higher AUC (Area Under the receiver operating Curve) than external evaluation causing overestimation of model accuracy and did not select the same models; models integrating sampling effort performed better with external validation. AUC, specificity, and sensitivity of models calculated with different filtered external datasets differed for some species. However, for most species, complementary fieldwork was not necessary to obtain coherent results, as long as the citizen science data were strongly filtered.
4. Since external validation methods using independent data are considered more robust, filtering data from citizen sciences may make a valuable contribution to the assessment of SDM. Limited complementary fieldwork with volunteer''s participation to complete ecological gradients may also possibly enhance citizen involvement and lead to better use of SDM in decision processes for nature conservation.

     

Assessing future habitat availability for coastal lowland anurans in the Brazilian Atlantic rainforest

Global warming is expected to cause several modifications to physical environments, and sea level rise is a certain outcome. However, assessment of the potential impacts caused by sea level rise on biodiversity is still emerging. Therefore, we assessed the combined impact of global warming and sea level rise on the potential distribution of 19 coastal lowland anurans in the biodiversity hotspot Atlantic Forest. We applied a correlative species distribution model (SDM) (BIOCLIM) and GIS-based spatial analyses. We evaluated the extent of changes of potential distributions under extreme and moderate global warming scenarios as well as two extreme sea level rise scenarios. Our results suggest wide areas of suitable habitat for most species in the future. However, for 15% of these species the SDMs predict massive losses of range extent as a result of a combination of global warming and sea level rise. Such observations highlight an immediate need to consider the potential effects of sea level rise in conservation action plans. Since the current potential distribution of these anuran species is likely underestimated, we also analyzed their environmental niche under current conditions in order to provide a baseline for further field surveys. Considering this current state of knowledge for such species, species distribution modeling to help gather further information on unknown species is desirable.     

A comparative evaluation of presence-only methods for modelling species distribution

In spite of increasing application of presence-only models in ecology and conservation and the growing number of such models, little is known about the relative performance of different modelling methods, and some of the leading models (e.g. GARP and ENFA) have never been compared with one another. Here we compare the performance of six presence-only models that have been selected to represent an increasing level of model complexity [BIOCLIM, HABITAT, Mahalanobis distance (MD), DOMAIN, ENFA, and GARP] using data on the distribution of 42 species of land snails, nesting birds, and insectivorous bats in Israel. The models were calibrated using data from museum collections and observation databases, and their predictions were evaluated using Cohen's Kappa based on field data collected in a standardized sampling design covering most parts of Israel. Predictive accuracy varied between modelling methods with GARP and MD showing the highest accuracy, BIOCLIM and ENFA showing the lowest accuracy, and HABITAT and DOMAIN showing intermediate accuracy levels. Yet, differences between the various models were relatively small except for GARP and MD that were significantly more accurate than BIOCLIM and ENFA. In spite of large differences among species in prevalence and niche width, neither prevalence nor niche width interacted with the modelling method in determining predictive accuracy. However, species with relatively narrow niches were modelled more accurately than species with wider niches. Differences among species in predictive accuracy were highly consistent over all modelling methods, indicating the need for a better understanding of the ecological and geographical factors that influence the performance of species distribution models.     

The Atlantic elements in the Swiss flora: distribution, diversity, and conservation status

The purpose of this study was to examine the composition, distribution, ecology, and conservation status of the Atlantic elements of the Swiss flora. About 195 Atlantic and 80 Mediterranean–Atlantic vascular plant species of the European flora have been used as the basis for our analysis. The complete list of 3,143 taxa has been used as the reference for the Swiss flora. The distributions of the species are illustrated in coincidence maps based on the computer database of the Data Centre of the Swiss Flora in Geneva, Switzerland. Our study demonstrates clearly that the Atlantic flora of Europe requires a new biogeographical appraisal. The Swiss flora comprises 66 Atlantic and Mediterranean–Atlantic taxa, which are taxonomically and ecologically highly diverse. Switzerland contains 44% of all European Sub-Atlantic plants. This confirms the Sub-Atlantic geographical position of Switzerland. Only one Eu-Atlantic species growing in Switzerland, Vicia orobus, can be classified as native with certainty. This species is critically endangered and merits the highest conservation priority. Although a very alpine country, Switzerland has a relatively large number of Mediterranean–Atlantic species. The Atlantic and Mediterranean–Atlantic plants are a very threatened group in Switzerland, with wetland plants the most imperilled ecological group. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.