Predicted effect of climate change on the invasibility and distribution of the Western corn root‐worm

• 1 Insect pests, biological invasions and climate change are considered to represent major threats to biodiversity, ecosystem functioning, agriculture and forestry. Deriving hypothesis of contemporary and/or future potential distributions of insect pests and invasive species is becoming an important tool for predicting the spatial structure of potential threats.
• 2 The western corn rootworm (WCR) Diabrotica virgifera virgifera LeConte is a pest of maize in North America that has invaded Europe in recent years, resulting in economic costs in terms of maize yields in both continents. The present study aimed to estimate the dynamics of potential areas of invasion by the WCR under a climate change scenario in the Northern Hemisphere. The areas at risk under this scenario were assessed by comparing, using complementary approaches, the spatial projections of current and future areas of climatic favourability of the WCR. Spatial hypothesis were generated with respect to the presence records in the native range of the WCR and physiological thresholds from previous empirical studies.
• 3 We used a previously developed protocol specifically designed to estimate the climatic favourability of the WCR. We selected the most biologically relevant climatic predictors and then used multidimensional envelope (MDE) and Mahalanobis distances (MD) approaches to derive potential distributions for current and future climatic conditions.
• 4 The results obtained showed a northward advancement of the upper physiological limit as a result of climate change, which might increase the strength of outbreaks at higher latitudes. In addition, both MDE and MD outputs predict the stability of climatic favourability for the WCR in the core of the already invaded area in Europe, which suggests that this zone would continue to experience damage from this pest in Europe.

     

TreeGOER: A database with globally observed environmental ranges for 48,129 tree species

The TreeGOER (Tree Globally Observed Environmental Ranges) database provides information for most known tree species of their environmental ranges for 38 bioclimatic, eight soil and three topographic variables. It is based on species distribution modelling analyses of more than 44 million occurrences. The database can be accessed from https://doi.org/10.5281/zenodo.7922927 . Statistics that include 5% and 95% quantiles were estimated for a cleaned and taxonomically standardized occurrence data set with different methods of outlier detection, with estimates for roughly 45% of species being based on 20 or more observation records. Where sufficient representative observations are available, the ranges provide useful preliminary estimates of suitable conditions particularly for lesser-known species under climate change. Inferred core bioclimatic ranges of species along global temperature and moisture index gradients and across continents follow the known global distribution of tree diversity such as its highest levels in moist tropical forests and the ‘odd man out’ pattern of lower levels in Africa. To demonstrate how global analyses for large numbers of tree species can easily be done in R with TreeGOER , here I present two case studies. The first case study investigated latitudinal trends of tree vulnerability and compared these with previous results obtained for urban trees. The second case study focused on tropical areas, compared trends in different longitudinal zones and investigated patterns for the moisture index. TreeGOER is expected to benefit researchers conducting biogeographical and climate change research for a wide range of tree species at a variety of spatial and temporal scales.     

Preliminary distributional analysis of US endangered bird species

A first exploration of applications of ecological niche modeling and geographic distributional prediction to endangered species protection is developed. Foci of richness of endangered bird species are identified in coastal California and along the southern fringe of the United States. Species included on the Endangered Species List on the basis of peripheral populations inflate these concentrations considerably. Species without protection in the US National Park System are focused particularly in peninsular Florida. Application of this methodology to additional taxa and regions holds promise for diverse conservation applications.     

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.     

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.     

Climate versus weather extremes: Temporal predictor resolution matters for future rather than current regional species distribution models

Aim

Climate is considered a major driver of species distributions. Long‐term climatic means are commonly used as predictors in correlative species distribution models (SDMs). However, this coarse temporal resolution does not reflect local conditions that populations experience, such as short‐term weather extremes, which may have a strong impact on population dynamics and local distributions. We here compare the performance of climate‐ and weather‐based predictors in regional SDMs and their influence on future predictions, which are increasingly used in conservation planning.

Location

South‐western Germany.

Methods

We built different SDMs for 20 Orthoptera species based on three predictor sets at a regional scale for current and future climate scenarios. We calculated standard bioclimatic variables and yearly and seasonal sets of climate change indicating variables of weather extremes. As the impact of extreme events may be stronger for habitat specialists than for generalists, we distinguished species’ degrees of specialization. We computed linear mixed‐effects models to identify significant effects of algorithm, predictor set and specialization on model performance and calculated correlations and geographical niche overlap between spatial predictions.

Results

Current predictions were rather similar among all predictor sets, but highly variable for future climate scenarios. Bioclimatic and seasonal weather predictors performed slightly better than yearly weather predictors, though performance differences were minor. We found no evidence that specialists are more sensitive to weather extremes than generalists.

Main conclusions

For future projections of species distributions, SDM predictor selection should not solely be based on current performances and predictions. As long‐term climate and short‐term weather predictors represent different environmental drivers of a species’ distribution, we argue to interpret diverging future projections as complements. Even if similar current performances and predictions might imply their equivalency, favouring one predictor set neglects important aspects of future distributions and might mislead conservation decisions based on them.

     

高盆樱桃与钟花樱桃的地理分布模拟及生态特征比较分析

该研究以高盆樱桃(Cerasus cerasoides)及其近缘种钟花樱桃(C. campanulata)为对象,通过收集其实际分布的地理坐标,运用BIOCLIM模型模拟现代适生区范围并预测其未来气候下(CCM3,2100)潜在分布区的变化; 结合主成分分析和相关性分析确定其主导气候因子,并比较两者在气候限制因子方面的差异; 利用受试者工作特征曲线(Receiver operation characteristic, ROC)评估模型预测效果。结果表明:(1)高盆樱桃和钟花樱桃主要分布于我国长江以南大部分省区,两者现代分布中心分布位于云贵高原,武夷山脉和南岭山脉。(2)未来气候变化情境下(CCM3),高盆樱桃和钟花樱桃的适生范围将缩小。二者在中国西南部(高盆樱桃)和东南部(钟花樱桃)的适生范围可能大幅减小,而钟花樱桃在湖南西部的适生区可能增加。(3)主成分(PCA)及相关性分析表明,年降水量(bio12)、最湿季降水量(bio16)、最暖季降水量(bio18)、温度季节变化方差(bio4)是影响高盆樱桃及钟花樱桃当下适生区的主要气候因子,“热量变异幅度”是造成二者分布存在差异的最主要环境因子。(4)钟花樱桃(0.816)和高盆樱桃(0.799)的AUC值均高于随机测试(0.500),说明BIOCLIM模型可以准确预测高盆樱桃及钟花樱桃的分布。这为高盆樱桃和钟花樱桃资源保护、物种鉴定和谱系地理学的研究提供重要指导。     

The Effects of Climate Change on the Long-Term Conservation of Fagus grandifolia var. mexicana, an Important Species of the Cloud Forest in Eastern Mexico

We examined the effects of climate change on the future conservation and distribution patterns of the cloud forests in eastern Mexico, by using as a species model to Fagus grandifolia Ehr. var. mexicana (Martínez) Little which is mainly located in this vegetation type, at the Sierra Madre Oriental. This species was selected because it is restricted to the cloud forest, where it is a dominant element and has not been considered for protection in any national or international law. It is probably threatened due to the fact that it plays an important social role as a source of food and furnishing. We used a floristic database and a bioclimatic modeling approach including 19 climatic parameters, in order to obtain the current potential distribution pattern of the species. Currently, its potential distribution pattern shows that it is distributed in six different Mexican Priority Regions for Conservation. In addition, we also selected a future climate scenario, on the basis of some climate changes predictions already proposed. The scenario proposed is characterized by +2 °C and −20% rainfall in the region. Under this predicted climatic condition, we found a drastic distribution contraction of the species, in which most of the remaining populations will inhabit restricted areas located outside the boundaries of the surrounding reserves. Consequently, our results highlight the importance of considering the effects of possible future climate changes on the selection of conservation areas and the urgency to conserve some remaining patches of existing cloud forests. Accordingly, we believe that our bioclimatic modeling approach represents a useful tool to undertake decisions concerning the definition of protected areas, once the current potential distribution pattern of some selected species is known.