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.     

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.     

Chironomid community structure deduced from larvae and pupal exuviae of a chalk stream

Species abundances of Chironomidae (Insecta: Diptera) have often been excluded from studies of benthic river communities because of difficulties associated with sampling and identifying larvae. Chironomid pupal exuviae are easier to collect and identify and could be used to determine community structure if shown to be representative of local larval assemblages. Larvae were sampled along a 20 m chain secured over mid-channel gravels, upstream of two collection points for pupal exuviae. Proportional taxa abundances of pupal exuviae and larvae sampled from 130 m of stream were directly compared by a ² test of independence and also separately fitted to four models of species abundance distribution. Observed proportions of taxa were not independent of the life stage sampled. The greatest discrepancies occurred with species of pupal exuviae that were absent as larvae from the gravel. The log series model provided the best fit with both pupal and larval data. Collections of pupal exuviae had greater species richness and evenness than samples of larvae. This was considered to be a consequence of sampling larvae from the gravel habitat alone.     

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.     

Why Would Plant Species Become Extinct Locally If Growing Conditions Improve?

Two assumptions underlie current models of the geographical ranges of perennial plant species: 1. current ranges are in equilibrium with the prevailing climate, and 2. changes are attributable to changes in macroclimatic factors, including tolerance of winter cold, the duration of the growing season, and water stress during the growing season, rather than to biotic interactions. These assumptions allow model parameters to be estimated from current species ranges. Deterioration of growing conditions due to climate change, e.g. more severe drought, will cause local extinction. However, for many plant species, the predicted climate change of higher minimum temperatures and longer growing seasons means, improved growing conditions. Biogeographical models may under some circumstances predict that a species will become locally extinct, despite improved growing conditions, because they are based on an assumption of equilibrium and this forces the species range to match the species-specific macroclimatic thresholds. We argue that such model predictions should be rejected unless there is evidence either that competition influences the position of the range margins or that a certain physiological mechanism associated with the apparent improvement in growing conditions negatively affects the species performance. We illustrate how a process-based vegetation model can be used to ascertain whether such a physiological cause exists. To avoid potential modelling errors of this type, we propose a method that constrains the scenario predictions of the envelope models by changing the geographical distribution of the dominant plant functional type. Consistent modelling results are very important for evaluating how changes in species areas affect local functional trait diversity and hence ecosystem functioning and resilience, and for inferring the implications for conservation management in the face of climate change.     

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.     

岛屿生物地理学理论：模型与应用

前言岛屿有许多显著特征,如地理隔离,生物类群简单。这些特点为重复性研究和统计学分析奠定了基础,从而有利于许多深入而细致的生物学研究。因此,岛屿为发展和检验自然选择、物种形成及演化,以及生物地理学和生态学诸领域的理论和假设,提供了重要的自然实验室。岛屿生物地理学理论(MacArthurwilson学说)即为岛屿生物学研究中所产生的著名理论之一。该理论发展之     

Evaluating and presenting uncertainty in model‐based unconstrained ordination

Variability in ecological community composition is often analyzed by recording the presence or abundance of taxa in sample units, calculating a symmetric matrix of pairwise distances or dissimilarities among sample units and then mapping the resulting matrix to a low‐dimensional representation through methods collectively called ordination. Unconstrained ordination only uses taxon composition data, without any environmental or experimental covariates, to infer latent compositional gradients associated with the sampling units. Commonly, such distance‐based methods have been used for ordination, but recently there has been a shift toward model‐based approaches. Model‐based unconstrained ordinations are commonly formulated using a Bayesian latent factor model that permits uncertainty assessment for parameters, including the latent factors that correspond to gradients in community composition. While model‐based methods have the additional benefit of addressing uncertainty in the estimated gradients, typically the current practice is to report point estimates without summarizing uncertainty. To demonstrate the uncertainty present in model‐based unconstrained ordination, the well‐known spider and dune data sets were analyzed and shown to have large uncertainty in the ordination projections. Hence to understand the factors that contribute to the uncertainty, simulation studies were conducted to assess the impact of additional sampling units or species to help inform future ordination studies that seek to minimize variability in the latent factors. Accurate reporting of uncertainty is an important part of transparency in the scientific process; thus, a model‐based approach that accounts for uncertainty is valuable. An R package, UncertainOrd , contains visualization tools that accurately represent estimates of the gradients in community composition in the presence of uncertainty.     

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.     

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.     

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.     

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.     

Conservation of Chinese Theaceae species under future climate and land use changes

Aim

Climate and land use changes are two major pervasive and growing global causes of rapid changes in the distribution patterns of biodiversity, challenging the future effectiveness of protected areas (PAs), which were mainly designed based on a static view of biodiversity. Therefore, evaluating the effectiveness of protected areas for protecting the species threatened by climate and land use change is critical for future biodiversity conservation.

Location

China.

Methods

Here, using distributions of 200 Chinese Theaceae species and ensemble species distribution models, we identified species threatened by future climate and land use change (i.e. species with predicted loss of suitable habitat ≥30%) under scenarios incorporating climate change, land use change and dispersal. We then estimate the richness distribution patterns of threatened species and identify priority conservation areas and conservation gaps of the current PA network.

Results

Our results suggest that 36.30%–51.85% of Theaceae species will be threatened by future climate and land use conditions and that although the threatened species are mainly distributed at low latitudes in China under both current and future periods, the mean richness of the threatened species per grid cell will decline by 0.826–3.188 species by the 2070s. Moreover, we found that these priority conservation areas are highly fragmented and that the current PA network only covers 14.21%–20.87% of the ‘areas worth exploring’ and 6.91%–7.91% of the ‘areas worth attention’.

Main Conclusions

Our findings highlight the necessity of establishing new protected areas and ecological corridors in priority conservation areas to protect the threatened species. Moreover, our findings also highlight the importance of taking into consideration the potential threatened species under future climate and land use conditions when designating priority areas for biodiversity conservation.     

Without quality presence–absence data,discrimination metrics such as TSS can be misleading measures of model performance

The discriminating capacity (i.e. ability to correctly classify presences and absences) of species distribution models (SDMs) is commonly evaluated with metrics such as the area under the receiving operating characteristic curve (AUC), the Kappa statistic and the true skill statistic (TSS). AUC and Kappa have been repeatedly criticized, but TSS has fared relatively well since its introduction, mainly because it has been considered as independent of prevalence. In addition, discrimination metrics have been contested because they should be calculated on presence–absence data, but are often used on presence‐only or presence‐background data. Here, we investigate TSS and an alternative set of metrics—similarity indices, also known as F‐measures. We first show that even in ideal conditions (i.e. perfectly random presence–absence sampling), TSS can be misleading because of its dependence on prevalence, whereas similarity/F‐measures provide adequate estimations of model discrimination capacity. Second, we show that in real‐world situations where sample prevalence is different from true species prevalence (i.e. biased sampling or presence‐pseudoabsence), no discrimination capacity metric provides adequate estimation of model discrimination capacity, including metrics specifically designed for modelling with presence‐pseudoabsence data. Our conclusions are twofold. First, they unequivocally impel SDM users to understand the potential shortcomings of discrimination metrics when quality presence–absence data are lacking, and we recommend obtaining such data. Second, in the specific case of virtual species, which are increasingly used to develop and test SDM methodologies, we strongly recommend the use of similarity/F‐measures, which were not biased by prevalence, contrary to TSS.     

The distributions of one invasive and two native crayfishes in relation to coarse‐scale natural and anthropogenic factors

1. Native crayfishes are often extirpated from portions of their range because of interactions with invasive species, anthropogenic alterations to environmental conditions or a combination of these factors. Our goal was to identify coarse‐scale natural and anthropogenic factors related to the current distributions of the invasive crayfish, Orconectes hylas, and two endemic crayfishes, Orconectes peruncus and Orconectes quadruncus in the St. Francis River drainage, Missouri, U.S.A. and to provide wider insights into the potential role of anthropogenic factors in facilitating species displacement. 2. We used classification trees to model coarse‐scale natural and anthropogenic environmental factors and their relation to the presence or absence of each species. Model results were then used to predict probability of presence for each species within each stream segment throughout the entire St. Francis River drainage. 3. Factors related to geology and soils were the best predictors of species distributions. A dichotomy of these factors explained much of the discrete distributions of the two native species. Agricultural‐related factors were identified as the most influential anthropogenic activity related to species distributions. All associations between the invasive species and anthropogenic factors were negative which suggested the invader was not likely to establish in heavily impacted areas. Overall, our models had high correct classification rates, and we were able to reliably predict the presence of the invader in the invaded drainage. 4. Given the negative associations of the invader with anthropogenic alterations at a coarse spatial scale, we believe other mechanisms are likely to be responsible for the widespread displacement of the two native species. These findings can be used to assist in conservation activities such as creation of refugia for native species and may direct future research to identify the mechanism(s) of species displacement.     

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.     

Potential Distribution and Risk Assessment of an Invasive Plant Species: A Case Study of Hymenachne amplexicaulis in Australia

Given the limited resources available for weed management, a strategic approach is required to give the “best bang for your buck.” The current study incorporates: (1) a model ensemble approach to identify areas of uncertainty and commonality regarding a species invasive potential, (2) current distribution of the invaded species, and (3) connectivity of systems to identify target regions and focus efforts for more effective management. Uncertainty in the prediction of suitable habitat for H. amplexicaulis (study species) in Australia was addressed in an ensemble-forecasting approach to compare distributional scenarios from four models (CLIMATCH; CLIMEX; boosted regression trees [BRT]; maximum entropy [Maxent]). Models were built using subsets of occurrence and environmental data. Catchment risk was determined through incorporating habitat suitability, the current abundance and distribution of H. amplexicaulis, and catchment connectivity. Our results indicate geographic differences between predictions of different approaches. Despite these differences a number of catchments in northern, central, and southern Australia were identified as high risk of invasion or further spread by all models suggesting they should be given priority for the management of H. amplexicaulis. The study also highlighted the utility of ensemble approaches in indentifying areas of uncertainty and commonality regarding the species’ invasive potential.     

Developing biodiverse plantings suitable for changing climatic conditions 1: Underpinning scientific methods

Summary Governments across Australia have long been investing in revegetation in an effort to restore biodiversity and, more recently, mitigate climate change. However, no readily available methods have been described to assist project leaders identify species and provenance material likely to be sustainable under the changing climatic conditions of coming decades. Focussing particularly on trees, as trees are important for biosequestration as well as for providing habitat for other native species, Paper 1 of this two part series briefly reviews species distribution models and growth simulation models that could provide the scientific underpinning to improve and refine selection processes. While these previous scientific studies provide useful insights into how trees may respond to climate change, it is concluded that a readily accessible and easy‐to‐use approach is required to consider the potential adaptability of the many trees, shrubs and ground cover species that may be needed for biodiverse plantings. In Part 2 of this paper, the Atlas of Living Australia is used to provide preliminary information to assist species selection by assessing the climatic range of individual species based on their current distributions and, where available, cultivated locations. While using the Atlas can assist current selections, ways are outlined in Part 2 in which more reliable selections for changing climatic conditions could be made, building on the methods described here.