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.     

Predicting fundamental and realized distributions based on thermal niche: A case study of a freshwater turtle

Species distribution models (SDM) have been broadly used in ecology to address theoretical and practical problems. Currently, there are two main approaches to generate SDMs: (i) correlative, which is based on species occurrences and environmental predictor layers and (ii) process-based models, which are constructed based on species' functional traits and physiological tolerances. The distributions estimated by each approach are based on different components of species niche. Predictions of correlative models approach species realized niches, while predictions of process-based are more akin to species fundamental niche. Here, we integrated the predictions of fundamental and realized distributions of the freshwater turtle Trachemys dorbigni. Fundamental distribution was estimated using data of T. dorbigni's egg incubation temperature, and realized distribution was estimated using species occurrence records. Both types of distributions were estimated using the same regression approaches (logistic regression and support vector machines), both considering macroclimatic and microclimatic temperatures. The realized distribution of T. dorbigni was generally nested in its fundamental distribution reinforcing theoretical assumptions that the species' realized niche is a subset of its fundamental niche. Both modelling algorithms produced similar results but microtemperature generated better results than macrotemperature for the incubation model. Finally, our results reinforce the conclusion that species realized distributions are constrained by other factors other than just thermal tolerances.     

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.     

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.     

Le Osservazioni Fitofenologiche Della Rete Italiana Nel 1929 (Annata VIII)

Abstract

Correlative techniques for estimating environmental requirements of species – variably termed ecological niche modeling or species distribution modeling – are becoming very popular tools for ecologists and biogeographers in understanding diverse aspects of biodiversity. These tools, however, are frequently applied in ways that do not fit well into knowledge frameworks in population ecology and biogeography, or into the realities of sampling biodiversity over real-world landscapes. We offer 10 “fixes” – adjustments to typical methodologies that will take into account population ecological and biogeographic frameworks to produce better models.     

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.     

Multinomial Distribution and Ascertainment Models

In this paper general ascertainment models are studied relaxing the strong assumption of complete dominance. Probabilitis of ascertaiment for both the complete and incomplete models depending on family size and register size for two types of affected individuals are derived.     

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.     

How to describe species richness patterns for bryophyte conservation?

A large amount of data for inconspicuous taxa is stored in natural history collections; however, this information is often neglected for biodiversity patterns studies. Here, we evaluate the performance of direct interpolation of museum collections data, equivalent to the traditional approach used in bryophyte conservation planning, and stacked species distribution models (S‐SDMs) to produce reliable reconstructions of species richness patterns, given that differences between these methods have been insufficiently evaluated for inconspicuous taxa. Our objective was to contrast if species distribution models produce better inferences of diversity richness than simply selecting areas with the higher species numbers. As model species, we selected Iberian species of the genus Grimmia (Bryophyta), and we used four well‐collected areas to compare and validate the following models: 1) four Maxent richness models, each generated without the data from one of the four areas, and a reference model created using all of the data and 2) four richness models obtained through direct spatial interpolation, each generated without the data from one area, and a reference model created with all of the data. The correlations between the partial and reference Maxent models were higher in all cases (0.45 to 0.99), whereas the correlations between the spatial interpolation models were negative and weak (−0.3 to −0.06). Our results demonstrate for the first time that S‐SDMs offer a useful tool for identifying detailed richness patterns for inconspicuous taxa such as bryophytes and improving incomplete distributions by assessing the potential richness of under‐surveyed areas, filling major gaps in the available data. In addition, the proposed strategy would enhance the value of the vast number of specimens housed in biological collections.     

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.     

Predicting global change impacts on plant species’ distributions: Future challenges

Given the rate of projected environmental change for the 21st century, urgent adaptation and mitigation measures are required to slow down the on-going erosion of biodiversity. Even though increasing evidence shows that recent human-induced environmental changes have already triggered species’ range shifts, changes in phenology and species’ extinctions, accurate projections of species’ responses to future environmental changes are more difficult to ascertain. This is problematic, since there is a growing awareness of the need to adopt proactive conservation planning measures using forecasts of species’ responses to future environmental changes.

There is a substantial body of literature describing and assessing the impacts of various scenarios of climate and land-use change on species’ distributions. Model predictions include a wide range of assumptions and limitations that are widely acknowledged but compromise their use for developing reliable adaptation and mitigation strategies for biodiversity. Indeed, amongst the most used models, few, if any, explicitly deal with migration processes, the dynamics of population at the “trailing edge” of shifting populations, species’ interactions and the interaction between the effects of climate and land-use.

In this review, we propose two main avenues to progress the understanding and prediction of the different processes occurring on the leading and trailing edge of the species’ distribution in response to any global change phenomena. Deliberately focusing on plant species, we first explore the different ways to incorporate species’ migration in the existing modelling approaches, given data and knowledge limitations and the dual effects of climate and land-use factors. Secondly, we explore the mechanisms and processes happening at the trailing edge of a shifting species’ distribution and how to implement them into a modelling approach. We finally conclude this review with clear guidelines on how such modelling improvements will benefit conservation strategies in a changing world.     

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.     

Estimating lead time and overdiagnosis associated with PSA screening from prostate cancer incidence trends

Summary .   The introduction of the prostate-specific antigen (PSA) test has led to dramatic changes in the incidence of prostate cancer in the United States. In this article, we use information on the increase and subsequent decline in prostate cancer incidence following the adoption of PSA to estimate the lead time associated with PSA screening. The lead time is a key determinant of the likelihood of overdiagnosis, one of the main costs associated with the PSA test. Our approach conceptualizes observed incidence as the sum of the secular trend in incidence, which reflects incidence in the absence of PSA, and the excess incidence over and above the secular trend, which is a function of population screening patterns and the unknown lead time. We develop a likelihood model for the excess incidence given the secular trend and use it to estimate the mean lead time under specified distributional assumptions. We also develop a likelihood model for observed incidence and use it to simultaneously estimate the mean lead time together with a smooth secular trend. Variances and confidence intervals are estimated via a parametric bootstrap. Our results indicate an average lead time of approximately 4.59 years (95% confidence interval [3.24, 5.93]) for whites and 6.78 years [5.42, 8.20] for blacks with a corresponding secular trend estimate that is fairly flat after the introduction of PSA screening. These estimates correspond to overdiagnosis frequencies of approximately 22.7% and 34.4% for screen-detected whites and blacks, respectively. Our results provide the first glimpse of a plausible secular trend in prostate cancer incidence and suggest that, in the absence of PSA screening, disease incidence would not have continued its historic increase, rather it would have leveled off in accordance with changes in prostate patterns of care unrelated to PSA.