Evaluating alternative data sets for ecological niche models of birds in the Andes

Ecological niche modeling (ENM) is an effective tool for providing innovative insights to questions in evolution, ecology and conservation. As environmental datasets accumulate, modelers need to evaluate the relative merit of different types of data for ENM. We used three alternative environmental data sets: climatic data, remote-sensing data (Normalized Difference Vegetation Index), and elevation data, to model the distribution of six bird species of the genus Grallaria in the Ecuadorian Andes. We assessed the performance of models created with each environmental data set and all possible combinations by comparing the geographic predictions of our models with detailed maps developed by expert ornithologists. Results varied depending on the specific measure of performance. Models including climate variables performed relatively well across most measures, whereas models using only NDVI performed poorly. Elevation based models were relatively good at predicting most sites of expected occurrence but showed a high over-prediction error. Combinations of data sets usually increased the performance of the models, but not significantly. Our results highlight the importance of including climatic variables in ENM and the simultaneous use of various data sets when possible. This strategy attenuates the effects of specific variables that decrease model performance. Remote-sensing data, such as NDVI, should be used with caution in topographically complex regions with heavy cloud-cover. Nonetheless, remote-sensing data have the potential to improve ENM. Finally, we suggest a priori designation of modeling purposes to define specific performance measures accordingly.     

Ecological niche modelling as a technique for assessing threats and setting conservation priorities for Asian slow lorises (Primates: Nycticebus)

Aim Data on geographical ranges are essential when defining the conservation status of a species, and in evaluating levels of human disturbance. Where locality data are deficient, presence‐only ecological niche modelling (ENM) can provide insights into a species’ potential distribution, and can aid in conservation planning. Presence‐only ENM is especially important for rare, cryptic and nocturnal species, where absence is difficult to define. Here we applied ENM to carry out an anthropogenic risk assessment and set conservation priorities for three threatened species of Asian slow loris (Primates: Nycticebus). Location Borneo, Java and Sumatra, Southeast Asia. Methods Distribution models were built using maximum entropy (MaxEnt) ENM. We input 20 environmental variables comprising temperature, precipitation and altitude, along with species locality data. We clipped predicted distributions to forest cover and altitudinal data to generate remnant distributions. These were then applied to protected area (PA) and human land‐use data, using specific criteria to define low‐, medium‐ or high‐risk areas. These data were analysed to pinpoint priority study sites, suitable reintroduction zones and protected area extensions. Results A jackknife validation method indicated highly significant models for all three species with small sample sizes (n = 10 to 23 occurrences). The distribution models represented high habitat suitability within each species’ geographical range. High‐risk areas were most prevalent for the Javan slow loris (Nycticebus javanicus) on Java, with the highest proportion of low‐risk areas for the Bornean slow loris (N. menagensis) on Borneo. Eighteen PA extensions and 23 priority survey sites were identified across the study region. Main conclusions Discriminating areas of high habitat suitability lays the foundations for planning field studies and conservation initiatives. This study highlights potential reintroduction zones that will minimize anthropogenic threats to animals that are released. These data reiterate the conclusion of previous research, showing MaxEnt is a viable technique for modelling species distributions with small sample sizes.     

Characterizing and predicting species distributions across environments and scales: Argentine ant occurrences in the eye of the beholder

Aim Species distribution models (SDMs) or, more specifically, ecological niche models (ENMs) are a useful and rapidly proliferating tool in ecology and global change biology. ENMs attempt to capture associations between a species and its environment and are often used to draw biological inferences, to predict potential occurrences in unoccupied regions and to forecast future distributions under environmental change. The accuracy of ENMs, however, hinges critically on the quality of occurrence data. ENMs often use haphazardly collected data rather than data collected across the full spectrum of existing environmental conditions. Moreover, it remains unclear how processes affecting ENM predictions operate at different spatial scales. The scale (i.e. grain size) of analysis may be dictated more by the sampling regime than by biologically meaningful processes. The aim of our study is to jointly quantify how issues relating to region and scale affect ENM predictions using an economically important and ecologically damaging invasive species, the Argentine ant (Linepithema humile). Location California, USA. Methods We analysed the relationship between sampling sufficiency, regional differences in environmental parameter space and cell size of analysis and resampling environmental layers using two independently collected sets of presence/absence data. Differences in variable importance were determined using model averaging and logistic regression. Model accuracy was measured with area under the curve (AUC) and Cohen's kappa. Results We first demonstrate that insufficient sampling of environmental parameter space can cause large errors in predicted distributions and biological interpretation. Models performed best when they were parametrized with data that sufficiently sampled environmental parameter space. Second, we show that altering the spatial grain of analysis changes the relative importance of different environmental variables. These changes apparently result from how environmental constraints and the sampling distributions of environmental variables change with spatial grain. Conclusions These findings have clear relevance for biological inference. Taken together, our results illustrate potentially general limitations for ENMs, especially when such models are used to predict species occurrences in novel environments. We offer basic methodological and conceptual guidelines for appropriate sampling and scale matching.     

Integrated Environmental and Genomic Analysis Reveals the Drivers of Local Adaptation in African Indigenous Chickens

Breeding for climate resilience is currently an important goal for sustainable livestock production. Local adaptations exhibited by indigenous livestock allow investigating the genetic control of this resilience. Ecological niche modeling (ENM) provides a powerful avenue to identify the main environmental drivers of selection. Here, we applied an integrative approach combining ENM with genome-wide selection signature analyses (XPEHH and Fst) and genotype−environment association (redundancy analysis), with the aim of identifying the genomic signatures of adaptation in African village chickens. By dissecting 34 agro-climatic variables from the ecosystems of 25 Ethiopian village chicken populations, ENM identified six key drivers of environmental challenges: One temperature variable—strongly correlated with elevation, three precipitation variables as proxies for water availability, and two soil/land cover variables as proxies of food availability for foraging chickens. Genome analyses based on whole-genome sequencing (n = 245), identified a few strongly supported genomic regions under selection for environmental challenges related to altitude, temperature, water scarcity, and food availability. These regions harbor several gene clusters including regulatory genes, suggesting a predominantly oligogenic control of environmental adaptation. Few candidate genes detected in relation to heat-stress, indicates likely epigenetic regulation of thermo-tolerance for a domestic species originating from a tropical Asian wild ancestor. These results provide possible explanations for the rapid past adaptation of chickens to diverse African agro-ecologies, while also representing new landmarks for sustainable breeding improvement for climate resilience. We show that the pre-identification of key environmental drivers, followed by genomic investigation, provides a powerful new approach for elucidating adaptation in domestic animals.     

基于MaxEnt模型对蜂巢奇露尾甲在中国的潜在分布研究

蜂巢奇露尾甲Aethina tumida Murray作为蜜蜂六大重要病原体之一,可对蜜蜂产业造成严重的经济损失。本研究明确蜂巢奇露尾甲在全国的适生区范围及主要环境影响因子,对其早期预警和检疫防治意义重大。根据蜂巢奇露尾甲现有的分布数据,筛选出主要的环境变量,通过MaxEnt模型、R语言软件、ENM Tools软件与ArcGIS软件预测蜂巢奇露尾甲在全国的适生区范围。结果表明：蜂巢奇露尾甲在我国的适生区范围主要在华南大部、华东大部、华中大部、西南大部、西北少部以及华北少部地区,高度适生区主要位于华南大部、华东大部、华中局部及西南局部地区。且最冷季度的平均温度和年降水量是限制蜂巢奇露尾甲潜在地理分布的重要环境变量。此预测结果可为今后对蜂巢奇露尾甲的预防与检疫提供理论依据。     

The uncertain nature of absences and their importance in species distribution modelling

Species distribution models (SDM) are commonly used to obtain hypotheses on either the realized or the potential distribution of species. The reliability and meaning of these hypotheses depends on the kind of absences included in the training data, the variables used as predictors and the methods employed to parameterize the models. Information about the absence of species from certain localities is usually lacking, so pseudo‐absences are often incorporated to the training data. We explore the effect of using different kinds of pseudo‐absences on SDM results. To do this, we use presence information on Aphodius bonvouloiri, a dung beetle species of well‐known distribution. We incorporate different types of pseudo‐absences to create different sets of training data that account for absences of methodological (i.e. false absences), contingent and environmental origin. We used these datasets to calibrate SDMs with GAMs as modelling technique and climatic variables as predictors, and compare these results with geographical representations of the potential and realized distribution of the species created independently. Our results confirm the importance of the kind of absences in determining the aspect of species distribution identified through SDM. Estimations of the potential distribution require absences located farther apart in the geographic and/or environmental space than estimations of the realized distribution. Methodological absences produce overall bad models, and absences that are too far from the presence points in either the environmental or the geographic space may not be informative, yielding important overestimations. GLMs and Artificial Neural Networks yielded similar results. Synthetic discrimination measures such as the Area Under the Receiver Characteristic Curve (AUC) must be interpreted with caution, as they can produce misleading comparative results. Instead, the joint examination of ommission and comission errors provides a better understanding of the reliability of SDM results.     

Mapping the observed and modelled intracontinental distribution of non-marine ostracods from South America

Ecological niche modelling (ENM) has been used to quantify the potential occurrence of species, by identifying the main environmental factors that determine the presence of species across geographical space. We provide a large-scale survey of the distribution of ostracod species in South America, by using the domains of 25 river basins. From 221 known ostracod species, we estimate the potential distribution of 61 species, using ENM. Ten clusters of potential distribution patterns were found. Clusters 8 and 9 grouped most of the species, which presented high similarity of niche between them. Heterocypris paningi Brehm, 1934 (group 1) obtained higher niche variability. The minimum temperatures of the coldest month and the mean elevation of the river basin were most important to predict the potential distribution of ostracods of most groups. South America has a complex pattern of elevation, which affects species distributions indirectly through changes in local factors. For instance, the Andes mountains might impose a barrier for ostracod distribution in the southern part of South America because of the low temperatures and precipitation. The ENM indicated that some regions and/or basins of South America might be susceptible to the entry of several ostracod species, presently absent, including non-native species.

     

提高生态位模型转移能力来模拟入侵物种 的潜在分布

生态位模型利用物种分布点所关联的环境变量去推算物种的生态需求, 模拟物种的分布。在模拟入侵物种分布时, 经典生态位模型包括模型构建于物种本土分布地, 然后将其转移并投射至另一地理区域, 来模拟入侵物种的潜在分布。然而在模型运用时, 出现了模型的转移能力较低、模拟的结果与物种的实际分布不相符的情况, 由此得出了生态位漂移等不恰当的结论。提高生态位模型的转移能力, 可以准确地模拟入侵物种的潜在分布, 为入侵种的风险评估提供参考。作者以入侵种茶翅蝽(Halyomorpha halys)和互花米草(Spartina alterniflora)为例, 从模型的构建材料(即物种分布点和环境变量)入手, 全面阐述提高模型转移能力的策略。在构建模型之前, 需要充分了解入侵物种的生物学特性、种群平衡状态、本土地理分布范围及物种的生物历史地理等方面的知识。在模型构建环节上, 物种分布点不仅要充分覆盖物种的地理分布和生态空间的范围, 同时要降低物种采样点偏差; 环境变量的选择要充分考虑其对物种分布的限制作用、各环境变量之间的空间相关性, 以及不同地理种群间生态空间是否一致, 同时要降低环境变量的空间维度; 模型构建区域要真实地反映物种的地理分布范围, 并考虑种群的平衡状态。作者认为, 在生态位保守的前提下, 如果模型是构建在一个合理方案的基础上, 生态位模型的转移能力是可以保证的, 在以模型转移能力较低的现象来阐述生态位分化时需要引起注意。     

Spatial genetic variation and habitat association of <Emphasis Type="Italic">Rhinichthys cataractae</Emphasis>, the longnose dace,in the Driftless Area of the upper Mississippi River basin

The Driftless Area of the upper Mississippi River drainage is a unique geographic region because of its complex geological history and the influence of recent, intensive human activities. The longnose dace, Rhinichthys cataractae, is a relatively common, small freshwater fish that is distributed in swift, cool streams within the region. The aim of this study was to determine the spatial genetic differentiation of the longnose dace and define the broad scale environmental variables that shape the distribution of the species in the southwestern portion of the Driftless Area. Genotypic data from seven microsatellite loci were analyzed for 276 individuals from 15 localities representing major drainages within the region in northeast Iowa. Broad scale environmental variables including hydrologic, soil, and climatic factors were evaluated to construct an ecological niche model (ENM) to predict the suitability of habitat for the species within the region. Results of the genetic analyses revealed two distinct, but somewhat admixed genetic clusters of longnose dace in Iowa. The genetic differentiation between localities and between drainages was low to moderate with some evidence of isolation by distance. Most of the variation was observed by differences between individuals within local populations. The ENM generated largely reflected the known distribution of the species in Iowa with a decreasing probability of suitable habitat from northern to southern drainages. Geologic factors played a key role in the model. The distribution and population structure of the longnose dace in the northeast Iowa revealed that isolation by distance, historical processes and the underlying geology are primarily responsible for the observed spatial distribution of genetic variation.     

Micro‐ or macroscale? Which one best predicts the establishment of an endemic Atlantic Forest palm?

Historically, macroecology and microecology have diverged with regard to the niche concept. A better understanding of functioning ecological systems, however, depends on an integrative approach to this concept at different spatial scales. A mixed approach, merging macro‐ and microscale by validating ecological niche modeling (ENM) with the results of in situ experiments and environmental data collection was used to understand if areas identified by ENM as highly suitable for adult palms are also adequate for seedling establishment. Syagrus weddelliana's (Arecaceae) distribution range falls within the Atlantic Rain Forest, and more specifically Serra dos Órgãos region (Rio de Janeiro state), southeastern Brazil. The following steps were performed: (a) ENM to delimit the area of occurrence of S. weddelliana and locate experimental areas; (b) a seed sowing experiment in areas with presence or absence of the species in areas of high or low environmental suitability at 36 experimental stations; and (c) characterization of each microhabitat which was related back to the macroscale results of ENM. Evidence of biotic and abiotic limitations was found for S. weddelliana distribution. Areas of higher suitability had lower seed predation rates and, consequently, higher seed germination rates. On the other hand, areas with low environmental suitability at the macroscale were divided into two types: areas with microhabitat similar to that of areas with high environmental suitability that had some germination despite high predation and areas with different environmental conditions that had no germination and high predation rates. Seedlings and adults had different abiotic requirements. Microhabitat conditions were more important for the initial establishment of S. weddelliana than macroclimatic variables. This finding demonstrates that macro‐ and microecological information works in a complementary way to a better understanding of the distribution of S. weddelliana.     

Is there a correlation between abundance and environmental suitability derived from ecological niche modelling? A meta‐analysis

It is thought that species abundance is correlated with environmental suitability and that environmental variables, scale, and type of model fitting can confound this relationship. We performed a meta‐analysis to 1) test whether species abundance is positively correlated with environmental suitability derived from correlative ecological niche models (ENM), 2) test whether studies encompassing large areas within a species range (> 50%) exhibited higher AS correlations than studies encompassing small areas within a species range (< 50%), 3) assess which modelling method provided higher AS correlation, and 4) compare strength of the AS relationship between studies using only climatic variables and those that used both climatic and other environmental variables to derive suitability. We used correlation coefficients to measure the relationship between abundance and environmental suitability derived from ENM. Each correlation coefficient was considered an effect size in a random‐effects multivariate meta‐analysis. In all cases we found a significantly positive relationship between abundance and suitability. This relationship was consistent regardless of scale of study, ENM method, or set of variables used to derive suitability. There was no difference in strength of correlation between studies focusing on large or small areas within a species’ range or among ENM methods. Studies using other variables in combination with climate exhibited higher AS correlations than studies using only climatic variables. We conclude that occurrence data can be a reasonable proxy for abundance, especially for vertebrates, and the use of local variables increases the strength of the AS relationship. Use of ENMs can significantly decrease survey costs and allow the study of large‐scale abundance patterns using less information. Including only climatic variables in ENM may confound the relationship between abundance and suitability when compared to studies including variables taken locally. However, modelers and conservationists must be aware that high environmental suitability does not always indicate high abundance.     

The speciation history and systematics of Carthamus (Asteraceae) with special emphasis on Turkish species by integrating phylogenetic and Ecological Niche Modelling data

To discuss the classification and possible scenarios for the speciation of Carthamus species in Turkey, 143 species occurrence data from Turkey used in Ecological Niche Modelling (ENM), ITS sequences of 23 available species gathered from the GenBank and current distribution information were used. The ENM was carried out by using MAXENT software. Among the 19 bioclimatic variables used in ENM, precipitation of coldest quarter (25 %), mean temperature of driest quarter (19 %) and annual precipitation (17 %) parameters have the highest percent contribution to the resulting prediction pattern, respectively. Bayesian-based phylogenetic analysis with divergence time estimation was implemented to obtain phylogenetic history of Carthamus species. Statistical dispersal–vicariance analysis and Bayesian binary MCMC analysis were also used to discuss biogeographical inferences. An identification key for Turkish Carthamus species that is in accordance with phylogenies was given. Ancestral area reconstruction analyses pointed out that the Western Asia region was the ancestral area for Carthamus species and in the Pliocene/Pleistocene period they started to diversify. Also ENM results clearly indicate that especially Anatolian species used Aegean and Mediterranean coastal part of Anatolia as potential refugia.     

Integrating Multiple Distribution Models to Guide Conservation Efforts of an Endangered Toad

Species distribution models are used for numerous purposes such as predicting changes in species’ ranges and identifying biodiversity hotspots. Although implications of distribution models for conservation are often implicit, few studies use these tools explicitly to inform conservation efforts. Herein, we illustrate how multiple distribution models developed using distinct sets of environmental variables can be integrated to aid in identification sites for use in conservation. We focus on the endangered arroyo toad (Anaxyrus californicus), which relies on open, sandy streams and surrounding floodplains in southern California, USA, and northern Baja California, Mexico. Declines of the species are largely attributed to habitat degradation associated with vegetation encroachment, invasive predators, and altered hydrologic regimes. We had three main goals: 1) develop a model of potential habitat for arroyo toads, based on long-term environmental variables and all available locality data; 2) develop a model of the species’ current habitat by incorporating recent remotely-sensed variables and only using recent locality data; and 3) integrate results of both models to identify sites that may be employed in conservation efforts. We used a machine learning technique, Random Forests, to develop the models, focused on riparian zones in southern California. We identified 14.37% and 10.50% of our study area as potential and current habitat for the arroyo toad, respectively. Generally, inclusion of remotely-sensed variables reduced modeled suitability of sites, thus many areas modeled as potential habitat were not modeled as current habitat. We propose such sites could be made suitable for arroyo toads through active management, increasing current habitat by up to 67.02%. Our general approach can be employed to guide conservation efforts of virtually any species with sufficient data necessary to develop appropriate distribution models.     

The identification and conservation of climate refugia for two Colombian endemic titi (Plecturocebus) monkeys

Natural resource managers face the challenge of developing conservation plans for key species and given that anthropogenic climate change (CC) effects on biodiversity are becoming increasingly evident, the new challenge is to properly incorporate CC adaptation strategies into such plans. Thus, the objective of this study is to evaluate the potential CC effects on the climatically suitable areas for two Colombian endemic titi monkeys Plecturocebus ornatus and P. caquetensis and to identify the prospective climate refugia as macro-ecological adaptation strategies for each species. A detailed ecological niche modeling (ENM) approach was applied with the maximum entropy algorithm, using presence records and different sets of bioclimatic variables describing baseline (1960–1990) and future climates (∼2070). Models of future climatic suitability were generated using projections of variables under a stabilization (RCP4.5) and business as usual (RCP8.5) scenarios with data from two general circulation models (GCMs) describing storylines of increasing (CESM1_CAM5) and decreasing (CSIRO_ACCESS1_3) rainfall patterns. The results for both species indicate that in a warmer future, opposite rainfall patterns and choice of the bioclimatic variables may lead to divergent responses on the extent and geographic distribution of their climatic niche, which varied from regions gaining, losing, and retaining suitability in potential climate refugia. Moreover, CC represents a serious threat for P. caquetensis and P. ornatus since their ranges may be largely exposed to novel climates. Their baseline climatic suitability area is projected to shrink and shift to higher elevations in the Andes mountains, and the climate refugia identified for both species are poorly covered by protected areas. Therefore, the climate refugia identified in this work and the management recommendations offered should be considered by species conservation plans to contribute to the selection of priority regions for conservation actions. The modeling approach reveals the uncertainties arising from the selection of bioclimatic variables and GCMs in ENM, which can be replicated to identify climate refugia targeting different species of conservation concern.     

Environmental and anthropogenic determinants of the spread of alien plant species: insights from South Africa’s quaternary catchments

Alien plants invasion has negative impacts on the structure and functionality of ecosystems. Understanding the determinants of this process is fundamental for addressing environmental issues, such as the water availability in South Africa’s catchments. Both environmental and anthropogenic factors determine the invasion of alien species; however, their relative importance has to be quantified. The aim of this paper was to estimate the importance of 32 explanatory variables in predicting the distribution of the major invasive alien plant species (IAPS) of South Africa, through the use of Species Distribution Models. We used data from the National Invasive Alien Plants Survey, delineated at a quaternary catchment level, coupled with climatic, land cover, edaphic, and anthropogenic variables. Using two-part generalized linear models, we compared the accuracy of two different sets of variables in predicting the spatial distribution of IAPS; the first included environmental correlates alone, and the second included both environmental and anthropogenic variables. Using Random Forest, we explored the relative importance of the variables in producing a map of potential distribution of IAPS. Results showed that the inclusion of anthropogenic variables did not significantly improve model predictions. The most important variables influencing the distribution of IAPS appeared to be the climatic ones. The modeled potential distribution was analyzed in relation to provinces, biomes, and species’ minimum residence time.     

Locating pleistocene refugia: comparing phylogeographic and ecological niche model predictions

Ecological niche models (ENMs) provide a means of characterizing the spatial distribution of suitable conditions for species, and have recently been applied to the challenge of locating potential distributional areas at the Last Glacial Maximum (LGM) when unfavorable climate conditions led to range contractions and fragmentation. Here, we compare and contrast ENM-based reconstructions of LGM refugial locations with those resulting from the more traditional molecular genetic and phylogeographic predictions. We examined 20 North American terrestrial vertebrate species from different regions and with different range sizes for which refugia have been identified based on phylogeographic analyses, using ENM tools to make parallel predictions. We then assessed the correspondence between the two approaches based on spatial overlap and areal extent of the predicted refugia. In 14 of the 20 species, the predictions from ENM and predictions based on phylogeographic studies were significantly spatially correlated, suggesting that the two approaches to development of refugial maps are converging on a similar result. Our results confirm that ENM scenario exploration can provide a useful complement to molecular studies, offering a less subjective, spatially explicit hypothesis of past geographic patterns of distribution.     

Global distribution patterns of highly pathogenic H5N1 avian influenza: Environmental vs. socioeconomic factors

In this report, we quantitatively analyzed the essential ecological factors that were strongly correlated with the global outbreak of highly pathogenic H5N1 avian influenza. The ecological niche modeling (ENM) was used to reveal the potential outbreak hotspots of H5N1. A two-step modeling procedure has been proposed: we first used BioClim model to obtain the coarse suitable areas of H5N1, and then those suitable areas with very high probabilities were retained as the inputs of multiple-variable autologistic regression analysis (MAR) for model refinement. MAR was implemented taking spatial autocorrelation into account. The final performance of ENM was evaluated using the areas under the curve (AUC) of receiver-operating characteristic. In addition, principal component analysis (PCA) was employed to reveal the most important variables and relevant ecological gradients of H5N1 outbreak. Niche visualization was used to identify potential spreading trend of H5N1 along important ecological gradients. For the first time, we combined socioeconomic and environmental variables as joint predictors in developing ecological niche modeling. Environmental variables represented the natural element related to H5N1 outbreak, whereas socioeconomic ones represented the anthropogenic element. Our results indicated that: (1) the high-risk hotspots are mainly located in temperate zones (indicated by ENM)—correspondingly, we argued that the “ecoregions hypothesis” was reasonable to some extent; (2) evaporation, humidity, human population density, livestock population density were the first four important factors (in descending order) that were associated with the H5N1 global outbreak (indicated by PCA); (3) influenza had a tendency to expand into areas with low evaporation (indicated by niche visualization). In conclusion, our study substantiates that both the environmental and socioeconomic variables jointly determined the global spreading trend of H5N1, but environmental variables played a more important role. Consequently, our study is consistent with the assumption that the natural element is more important than the anthropogenic element as the underlying ecological mechanisms explaining global H5N1 transmission.     

生态位模型在流行病学中的应用

随着新冠肺炎(COVID-19)疫情在全球逐渐开始蔓延, 对其传播范围以及强度的风险评估工作越来越受到人们的重视。作为生态学和生物地理学中常用的研究手段, 生态位模型也被应用到该项工作中来。虽然预测流行病的传播热点和趋势是生态位模型的应用方向之一, 但由于新冠病毒(SARS-CoV-2)自身特点, 生态位模型并非预测其潜在传播范围的有力工具。本文回顾了近些年来生态位模型在各种流行病学研究中的应用, 比较了疫病传播中常用生态位建模方法的优势与不足, 分析了适用生态位建模的疫病案例以及不适用于生态位建模的疫病特点, 明确指出, 生态位模型只能用于分析流行病在传播过程中受自然环境干扰的部分, 如中间宿主的潜在分布等。而对于包括COVID-19在内的主要通过人传人的流行病, 生态位模型尚无有效的手段进行预测。尽管生态位模型可用于分析流行病的传播范围, 但在使用时需要根据疾病特点有针对性地选择合适的建模方法与建模对象。为了量化疫病传播风险, 还需要考虑其他干扰因素, 以便准确测试和评估生态位模型。若不加选择地滥用生态位模型的工具, 反而会误导决策者的判断。总之, 在应用生态位模型进行研究工作, 特别是预测流行病的传播范围时, 首先要考虑建模对象是否满足生态学假设。     

Environmental Heterogeneity Explains the Genetic Structure of Continental and Mediterranean Populations of Fraxinus angustifolia Vahl

Tree species with wide distributions often exhibit different levels of genetic structuring correlated to their environment. However, understanding how environmental heterogeneity influences genetic variation is difficult because the effects of gene flow, drift and selection are confounded. We investigated the genetic variation and its ecological correlates in a wind-pollinated Mediterranean tree species, Fraxinus angustifolia Vahl, within a recognised glacial refugium in Croatia. We sampled 11 populations from environmentally divergent habitats within the Continental and Mediterranean biogeographical regions. We combined genetic data analyses based on nuclear microsatellite loci, multivariate statistics on environmental data and ecological niche modelling (ENM). We identified a geographic structure with a high genetic diversity and low differentiation in the Continental region, which contrasted with the significantly lower genetic diversity and higher population divergence in the Mediterranean region. The positive and significant correlation between environmental and genetic distances after controlling for geographic distance suggests an important influence of ecological divergence of the sites in shaping genetic variation. The ENM provided support for niche differentiation between the populations from the Continental and Mediterranean regions, suggesting that contemporary populations may represent two divergent ecotypes. Ecotype differentiation was also supported by multivariate environmental and genetic distance analyses. Our results suggest that despite extensive gene flow in continental areas, long-term stability of heterogeneous environments have likely promoted genetic divergence of ashes in this region and can explain the present-day genetic variation patterns of these ancient populations.     

Spatial perspectives enhance modeling of nanomaterial risks

Novel engineered nanomaterials (ENMs) are increasingly being manufactured and integrated into renewable energy generation and storage technologies. Past research estimated the potential impact of this increased demand on environmental systems, due to both the life cycle impact of ENM production and the potential for their direct release into ecosystems. However, many models treat ENM production and use as spatially implicit, without considering the specific geographic location of potential emissions. By not considering geographical context, ENM accumulation or impact may be underestimated. Here, we introduce an integrated predictive model that forecasts likely ENM manufacturing locations and potential emissions to the environment, with a focus on critical environmental areas and freshwater ecosystems. Spatially explicit ENM concentrations are estimated for four case study ENMs that have promising application in lithium‐ion battery production. Results demonstrate that potential ENM exposure from manufacturing locations within buffer zones of sensitive ecosystems would accumulate to levels associated with measured ecotoxicity risk under high release scenarios, underscoring the importance of adding a spatial and temporal perspective to life cycle toxicity impact assessment. This predictive integrated modeling approach is novel to the nanomaterial literature and can be adapted to other regions and material case studies to proactively inform life cycle tradeoffs and decision‐making.