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.     

A simple method to estimate the probable distribution of species

Species distribution models (SDMs) are broadly used to predict species distributions from available presence data. However, SDMs results have been criticized for several reasons mainly related to two basic characteristics of most SDMs: 1) general lack of reliable species absence information, 2) the frequent use of an arbitrary geographical extent (GE) or accessible area of the species. These impediments have motivated us to generate a procedure called niche of occurrence (NOO). NOO provides the probable distribution of species (realized niche) relying solely on partial information about presence of species. It operates within a natural geographical extent delimited by available observations and avoids using misleading thresholds to obtain binary presence–absence estimations when the species prevalence is unknown. In this study the main characteristics of NOO are presented, comparing its performance with other recognized and more complex SDMs by using virtual species to avoid the omnipresent error sources of real data sets.     

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.     

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.     

Changes in the realized niche of the invasive succulent CAM plant Furcraea foetida

Furcraea foetida (Asparagaceae) is a native plant of Central America and northern South America but there is no information about its country of origin. The species was introduced into Brazil and is now considered invasive, particularly in coastal ecosystems. To date, nothing is known about the environmental factors that constrain its distribution and there is only inconclusive information about its location of origin. We used reciprocal distribution models (RDM) to assess invasion risk of F. foetida across Brazil and to identify source regions in its native range. We also tested the niche conservatism hypothesis using Principal Components Analyses and statistical tests of niche equivalency and similarity between its native and invaded ranges. For RDM analysis, we built two models using maximum entropy, one using records in the native range to predict the invaded distribution (forward‐Ecological Niche Model or forward‐ENM) and one using records in the invaded range to predict the native distribution (reverse‐ENM). Forward‐ENM indicated invasion risk in the Cerrado region and the innermost region of the Atlantic Forest, however, failed to predict the current occurrence in southern Brazil. Reverse‐ENM supported an existing hypothesis that F. foetida originated in the Orinoco river basin, Amazon basin and Caribbean islands. Prediction errors in the RDM and multivariate analysis indicated that the species expanded its realized niche in Brazil. The niche similarity test further suggested that the niche differences are because of differences in habitat availability between the two ranges, not because of evolutionary changes. We hypothesize that physiological pre‐adaptation (especially, the crassulacean acid metabolism), human‐driven propagule pressure and high competitive ability are the main factors determining the current spatial distribution of the species in Brazil. Our study highlights the need to include F. foetida in plant invasion monitoring programs, especially in priority conservation areas where the species has still not been introduced.     

应用Maxent生态位模型预测淡水钩虾(甲壳纲,端足目)的分布(英文)

预测物种的适生区对于物种资源的评估、保护以及生物多样性的管理非常重要。由于全球气候变化和人类的过度开发,冷水性无脊椎动物的衰减速度比在陆地和海洋生活的无脊椎动物都要高。目前关于中国淡水钩虾分布方面的研究很少,本研究基于103个地表淡水钩虾的不同分布位点和广布种湖泊钩虾Gammarus lacustris 23个不同分布位点以及32个环境因子数据,使用生态位模型(Maxent)预测了淡水钩虾和湖泊钩虾在我国的适生分布区域。结果显示淡水钩虾非常适合分布在我国的一些偏远山区,如长白山、太行山、横断山、天山、昆仑山和祁连山,而青藏高原的东部、西部边缘地区和南部分布地区、尼泊尔、不丹和朝鲜半岛也是淡水钩虾的潜在适生区域,但淡水钩虾在我国华南、华中和华北的平原地区分布却很少,其在我国的潜在分布区与-10℃和5℃1月平均气温线间的区域相似。淡水钩虾是典型的狭温性物种,在不适宜温度条件下很难存活,这可能也是限制其扩散和存活的关键性因素。     

North by north‐west: climate change and directions of density shifts in birds

There is increasing evidence that climate change shifts species distributions towards poles and mountain tops. However, most studies are based on presence–absence data, and either abundance or the observation effort has rarely been measured. In addition, hardly any studies have investigated the direction of shifts and factors affecting them. Here, we show using count data on a 1000 km south–north gradient in Finland, that between 1970–1989 and 2000–2012, 128 bird species shifted their densities, on average, 37 km towards the north north‐east. The species‐specific directions of the shifts in density were significantly explained by migration behaviour and habitat type. Although the temperatures have also moved on average towards the north north‐east (186 km), the species‐specific directions of the shifts in density and temperature did not correlate due to high variation in density shifts. Findings highlight that climate change is unlikely the only driver of the direction of species density shifts, but species‐specific characteristics and human land‐use practices are also influencing the direction. Furthermore, the alarming results show that former climatic conditions in the north‐west corner of Finland have already moved out of the country. This highlights the need for an international approach in research and conservation actions to mitigate the impacts of climate change.     

The biogeography of prediction error: why does the introduced range of the fire ant over‐predict its native range?

Aim The use of species distribution models (SDMs) to predict biological invasions is a rapidly developing area of ecology. However, most studies investigating SDMs typically ignore prediction errors and instead focus on regions where native distributions correctly predict invaded ranges. We investigated the ecological significance of prediction errors using reciprocal comparisons between the predicted invaded and native range of the red imported fire ant (Solenopsis invicta) (hereafter called the fire ant). We questioned whether fire ants occupy similar environments in their native and introduced range, how the environments that fire ants occupy in their introduced range changed through time relative to their native range, and where fire ant propagules are likely to have originated. Location We developed models for South America and the conterminous United States (US) of America. Methods We developed models using the Genetic Algorithm for Rule‐set Prediction (GARP) and 12 environmental layers. Occurrence data from the native range in South America were used to predict the introduced range in the US and vice versa. Further, time‐series data recording the invasion of fire ants in the US were used to predict the native range. Results Native range occurrences under‐predicted the invasive potential of fire ants, whereas occurrence data from the US over‐predicted the southern boundary of the native range. Secondly, introduced fire ants initially established in environments similar to those in their native range, but subsequently invaded harsher environments. Time‐series data suggest that fire ant propagules originated near the southern limit of their native range. Conclusions Our findings suggest that fire ants from a peripheral native population established in an environment similar to their native environment, and then ultimately expanded into environments in which they are not found in their native range. We argue that reciprocal comparisons between predicted native and invaded ranges will facilitate a better understanding of the biogeography of invasive and native species and of the role of SDMs in predicting future distributions.     

生态系统中Lyapunov函数的构造

本文讨论生态模型中Liapunov函数的构造方法．典型的Liapunov函数的构造步骤和某些应用被综述．     

Dispersal ability correlates with range size in Amazonian habitat-restricted birds

Understanding how species attain their geographical distributions and identifying traits correlated with range size are important objectives in biogeography, evolutionary biology and biodiversity conservation. Despite much effort, results have been varied and general trends have been slow to emerge. Studying species pools that occupy specific habitats, rather than clades or large groupings of species occupying diverse habitats, may better identify ranges size correlates and be more informative for conservation programmes in a rapidly changing world. We evaluated correlations between a set of organismal traits and range size in bird species from Amazonian white-sand ecosystems. We assessed if results are consistent when using different data sources for phylogenetic and range hypotheses. We found that dispersal ability, as measured by the hand-wing index, was correlated with range size in both white-sand birds and their non-white-sand sister taxa. White-sand birds had smaller ranges on average than their sister taxa. The results were similar and robust to the different data sources. Our results suggest that the patchiness of white-sand ecosystems limits species’ ability to reach new habitat islands and establish new populations.     

Distribution models for koalas in South Australia using citizen science‐collected data

The koala (Phascolarctos cinereus) occurs in the eucalypt forests of eastern and southern Australia and is currently threatened by habitat fragmentation, climate change, sexually transmitted diseases, and low genetic variability throughout most of its range. Using data collected during the Great Koala Count (a 1-day citizen science project in the state of South Australia), we developed generalized linear mixed-effects models to predict habitat suitability across South Australia accounting for potential errors associated with the dataset. We derived spatial environmental predictors for vegetation (based on dominant species of Eucalyptus or other vegetation), topographic water features, rain, elevation, and temperature range. We also included predictors accounting for human disturbance based on transport infrastructure (sealed and unsealed roads). We generated random pseudo-absences to account for the high prevalence bias typical of citizen-collected data. We accounted for biased sampling effort along sealed and unsealed roads by including an offset for distance to transport infrastructures. The model with the highest statistical support (wAIC_c ∼ 1) included all variables except rain, which was highly correlated with elevation. The same model also explained the highest deviance (61.6%), resulted in high R²(m) (76.4) and R²(c) (81.0), and had a good performance according to Cohen''s κ (0.46). Cross-validation error was low (∼ 0.1). Temperature range, elevation, and rain were the best predictors of koala occurrence. Our models predict high habitat suitability in Kangaroo Island, along the Mount Lofty Ranges, and at the tips of the Eyre, Yorke and Fleurieu Peninsulas. In the highest-density region (5576 km²) of the Adelaide–Mount Lofty Ranges, a density–suitability relationship predicts a population of 113,704 (95% confidence interval: 27,685–199,723; average density = 5.0–35.8 km⁻²). We demonstrate the power of citizen science data for predicting species'' distributions provided that the statistical approaches applied account for some uncertainties and potential biases. A future improvement to citizen science surveys to provide better data on search effort is that smartphone apps could be activated at the start of the search. The results of our models provide preliminary ranges of habitat suitability and population size for a species for which previous data have been difficult or impossible to gather otherwise.     

Habitat modelling of tracking data from multiple marine predators identifies important areas in the Southern Indian Ocean

Aim

The distribution of marine predators is driven by the distribution and abundance of their prey; areas preferred by multiple marine predator species should therefore indicate areas of ecological significance. The Southern Ocean supports large populations of seabirds and marine mammals and is undergoing rapid environmental change. The management and conservation of these predators and their environment relies on understanding their distribution and its link with the biophysical environment, as the latter determines the distribution and abundance of prey. We addressed this issue using tracking data from 14 species of marine predators to identify important habitat.

Location

Indian Ocean sector of the Southern Ocean.

Methods

We used tracking data from 538 tag deployments made over a decade at the Subantarctic Prince Edward Islands. For each real track, we simulated a set of pseudo‐tracks that allowed a presence‐availability habitat modelling approach that estimates an animal's habitat preference. Using model ensembles of boosted regression trees and random forests, we modelled these tracks as a response to a set of 17 environmental variables. We combined the resulting species‐specific models to evaluate areas of mean importance.

Results

Real tracking locations covered 39.75 million km², up to 7,813 km from the Prince Edward Islands. Areas of high mean importance were located broadly from the Subtropical Zone to the Polar Frontal Zone in summer and from the Subantarctic to Antarctic Zones in winter. Areas of high mean importance were best predicted by factors including wind speed, sea surface temperature, depth and current speed.

Main conclusions

The models and predictions developed here identify important habitat of marine predators around the Prince Edward Islands and can support the large‐scale conservation and management of Subantarctic ecosystems and the marine predators they sustain. The results also form the basis of future efforts to predict the consequences of environmental change.

     

A combination of hydrodynamical and statistical modelling reveals non-stationary climate effects on fish larvae distributions

Biological processes and physical oceanography are often integrated in numerical modelling of marine fish larvae, but rarely in statistical analyses of spatio-temporal observation data. Here, we examine the relative contribution of inter-annual variability in spawner distribution, advection by ocean currents, hydrography and climate in modifying observed distribution patterns of cod larvae in the Lofoten-Barents Sea. By integrating predictions from a particle-tracking model into a spatially explicit statistical analysis, the effects of advection and the timing and locations of spawning are accounted for. The analysis also includes other environmental factors: temperature, salinity, a convergence index and a climate threshold determined by the North Atlantic Oscillation (NAO). We found that the spatial pattern of larvae changed over the two climate periods, being more upstream in low NAO years. We also demonstrate that spawning distribution and ocean circulation are the main factors shaping this distribution, while temperature effects are different between climate periods, probably due to a different spatial overlap of the fish larvae and their prey, and the consequent effect on the spatial pattern of larval survival. Our new methodological approach combines numerical and statistical modelling to draw robust inferences from observed distributions and will be of general interest for studies of many marine fish species.     

The distribution of cultivated species of Porophyllum (Asteraceae) and their wild relatives under climate change

The geographic distribution of plant species is already being affected by climate change. Cropping patterns of edible plant species and their wild relatives will also be affected, making it important to predict possible changes to their distributions in the future. Currently, species distribution models are valuable tools that allow the estimation of species’ potential distributions, in the recent past as well as during other time spans for which climate data have been obtained. With the aim of evaluating how species distributions respond to current and future climate changes, in this work species distribution models were generated for two cultivated species of the Porophyllum genus (Asteraceae), known commonly as ‘pápalos' or ‘pápaloquelites', as well as their Mexican wild relatives, at five points in time (21,000 years ago, present, 2020, 2050, and 2080). Using a database of 1442 entries for 16 species of Porophyllum and 19 environmental variables, species distribution models were constructed for each time period using the Maxent modelling algorithm; those constructed for the future used a severe climate change scenario. The results demonstrate contrasting effects between the two cultivated species; for P. linaria, the future scenario suggests a decrease in distribution area, while for P. macrocephalum distribution is predicted to increase. Similar trends are observed in their wild relatives, where 11 species will tend to decrease in distribution area, while three are predicted to increase. It is concluded that the most important agricultural areas where the cultivated species are grown will not be greatly affected, while the areas inhabited by the wild species will. However, while the results suggest that climate change will affect the distribution of the cultivated species in contrasting ways, evaluations at finer scales are recommended to clarify the impact within cultivation zones.