Host plant associations and geography interact to shape diversification in a specialist insect herbivore

Disentangling the processes underlying geographic and environmental patterns of biodiversity challenges biologists as such patterns emerge from eco‐evolutionary processes confounded by spatial autocorrelation among sample units. The herbivorous insect, Belonocnema treatae (Hymenoptera: Cynipidae), exhibits regional specialization on three plant species whose geographic distributions range from sympatry through allopatry across the southern United States. Using range‐wide sampling spanning the geographic ranges of the three host plants and genotyping‐by‐sequencing of 1,217 individuals, we tested whether this insect herbivore exhibited host plant‐associated genomic differentiation while controlling for spatial autocorrelation among the 58 sample sites. Population genomic structure based on 40,699 SNPs was evaluated using the hierarchical Bayesian model entropy to assign individuals to genetic clusters and estimate admixture proportions. To control for spatial autocorrelation, distance‐based Moran's eigenvector mapping was used to construct regression variables summarizing spatial structure inherent among sample sites. Distance‐based redundancy analysis (dbRDA) incorporating the spatial variables was then applied to partition host plant‐associated differentiation (HAD) from spatial autocorrelation. By combining entropy and dbRDA to analyse SNP data, we unveiled a complex mosaic of highly structured differentiation within and among gall‐former populations finding evidence that geography, HAD and spatial autocorrelation all play significant roles in explaining patterns of genomic differentiation in B. treatae. While dbRDA confirmed host association as a significant predictor of patterns of genomic variation, spatial autocorrelation among sites explained the largest proportion of variation. Our results demonstrate the value of combining dbRDA with hierarchical structural analyses to partition spatial/environmental patterns of genomic variation.     

GENETIC CONSEQUENCES OF SEED DISPERSAL IN THREE SYMPATRIC FOREST HERBS. II. MICROSPATIAL GENETIC STRUCTURE WITHIN POPULATIONS

Significant spatial genetic differentiation over short distances was detected by F-statistics and spatial autocorrelation within populations of the temperate forest herbs Cryptotaenia canadensis, Osmorhiza claytonii and Sanicula odorata (Apiaceae). Differences among the three species were consistent with estimates of their seed-dispersal abilities. Populations of Cryptotaenia, with the most limited seed dispersal, are characterized by genetic structure at smaller spatial scales than those of Osmorhiza or Sanicula, as indicated by higher estimates of θ(F_st), larger autocorrelation coefficients, and correlograms with more distant x-intercepts. Although spatial autocorrelation was somewhat more sensitive to the distribution of rare alleles than F-statistics, the two methods were generally concordant. Genetic structure was more pronounced, and inbreeding coefficients larger, in low-density, patchy populations than in a high-density site. Observed patterns of spatial autocorrelation, particularly for Cryptotaenia, were in agreement with expectations based on simulations of isolation by distance. The magnitude of observed autocorrelations was less than those typically produced in computer-simulation studies, but this discrepancy between empirical and theoretical results probably is derived from a lack of genetic and demographic equilibrium in natural populations. Isolation by distance can be an important evolutionary force organizing spatial genetic structure in plant populations, particularly in predominantly self-fertilizing species such as those studied here.     

Spatial regression techniques for inter‐population data: studying the relationships between morphological and environmental variation

Understanding the importance of environmental dimensions behind the morphological variation among populations has long been a central goal of evolutionary biology. The main objective of this study was to review the spatial regression techniques employed to test the association between morphological and environmental variables. In addition, we show empirically how spatial regression techniques can be used to test the association of cranial form variation among worldwide human populations with a set of ecological variables, taking into account the spatial autocorrelation in data. We suggest that spatial autocorrelation must be studied to explore the spatial structure underlying morphological variation and incorporated in regression models to provide more accurate statistical estimates of the relationships between morphological and ecological variables. Finally, we discuss the statistical properties of these techniques and the underlying reasons for using the spatial approach in population studies.     

中国栗疫病菌群体遗传结构的空间自相关性分析

应用空间自相关分析方法对中国栗疫病菌17个居群RAPD遗传变异的空间结构进行研究,以探讨栗疫病菌居群遗传变异的分布特征及其形成机制。结果表明：中国栗疫病菌居群缺乏空间结构,绝大多数RAPD位点变异为随机分布的空间模式,但部分位点表现出渐变、斑块和双向渐变的非随机分布模式,又显示了一定的空间结构。推测其形成原因可能是长距离的基因流、人类活动、地理隔离以及栗疫病菌本身的繁殖特性综合作用的结果,并依据部分位点呈单向渐变的模式推测西南地区为中国栗疫病菌的起源中心。     

A classification of European skulls from three time periods

We analyze the taxonomic structure of European populations at three time periods, the Early Middle Ages, the Late Middle Ages and the Recent Period. The data consist of sample means for 10 cranial variables based on 137, 108, and 183 samples for the three periods. Clustering by standard numerical taxonomic procedures reveals that the data are represented only poorly as hierarchic classifications. The clusters form significant and moderately strong associations with an arrangement of the samples by regions (geography) and by language family. Whereas during the early period, language family showed a stronger association with clusters based on cranial morphology, in the recent populations these clusters correspond better with geography than with language. Ordinations of these populations by means of nonmetric multidimensional scaling shows the continuity of the taxonomic structure at all three periods. Only a few populations are outliers. The relations between phenetic distances (cranial morphology), geography, and language are examined by means of multiple Mantel tests. At all three periods geography is correlated somewhat more strongly with phenetics than is language affiliation, but the correlation with the latter increases with time. When the data are pooled over the three periods, the populations tend to group by language affiliation more than they do by period. Ordination of the pooled data reveals language patterns rather than patterns due to period, showing strong shifts in cranial measurements through time. These analyses show that while there is no clear-cut taxonomic structure in European populations that would justify the traditional classifications based on the crania, there are significant and important associations with both language affiliation, geography, and time period, in this order. These patterns are likely to have become established through the migration and subsequent expansion of populations into their areas of occupation during the time interval studied rather than by geographic differentiation in situ.     

Demographic fluctuations lead to rapid and cyclic shifts in genetic structure among populations of an alpine butterfly,Parnassius smintheus

Many populations, especially in insects, fluctuate in size, and periods of particularly low population size can have strong effects on genetic variation. Effects of demographic bottlenecks on genetic diversity of single populations are widely documented. Effects of bottlenecks on genetic structure among multiple interconnected populations are less studied, as are genetic changes across multiple cycles of demographic collapse and recovery. We take advantage of a long‐term data set comprising demographic, genetic and movement data from a network of populations of the butterfly, Parnassius smintheus, to examine the effects of fluctuating population size on spatial genetic structure. We build on a previous study that documented increased genetic differentiation and loss of spatial genetic patterns (isolation by distance and by intervening forest cover) after a network‐wide bottleneck event. Here, we show that genetic differentiation was reduced again and spatial patterns returned to the system extremely rapidly, within three years (i.e. generations). We also show that a second bottleneck had similar effects to the first, increasing differentiation and erasing spatial patterns. Thus, bottlenecks consistently drive random divergence of allele frequencies among populations in this system, but these effects are rapidly countered by gene flow during demographic recovery. Our results reveal a system in which the relative influence of genetic drift and gene flow continually shift as populations fluctuate in size, leading to cyclic changes in genetic structure. Our results also suggest caution in the interpretation of patterns of spatial genetic structure, and its association with landscape variables, when measured at only a single point in time.     

Quantitative approaches in climate change ecology

Contemporary impacts of anthropogenic climate change on ecosystems are increasingly being recognized. Documenting the extent of these impacts requires quantitative tools for analyses of ecological observations to distinguish climate impacts in noisy data and to understand interactions between climate variability and other drivers of change. To assist the development of reliable statistical approaches, we review the marine climate change literature and provide suggestions for quantitative approaches in climate change ecology. We compiled 267 peer‐reviewed articles that examined relationships between climate change and marine ecological variables. Of the articles with time series data (n = 186), 75% used statistics to test for a dependency of ecological variables on climate variables. We identified several common weaknesses in statistical approaches, including marginalizing other important non‐climate drivers of change, ignoring temporal and spatial autocorrelation, averaging across spatial patterns and not reporting key metrics. We provide a list of issues that need to be addressed to make inferences more defensible, including the consideration of (i) data limitations and the comparability of data sets; (ii) alternative mechanisms for change; (iii) appropriate response variables; (iv) a suitable model for the process under study; (v) temporal autocorrelation; (vi) spatial autocorrelation and patterns; and (vii) the reporting of rates of change. While the focus of our review was marine studies, these suggestions are equally applicable to terrestrial studies. Consideration of these suggestions will help advance global knowledge of climate impacts and understanding of the processes driving ecological change.     

Similarity in seasonal flow regimes,not regional environmental classifications explain synchrony in brown trout population dynamics in France

相似文献   

Red-shifts and red herrings in geographical ecology

I draw attention to the need for ecologists to take spatial structure into account more seriously in hypothesis testing. If spatial autocorrelation is ignored, as it usually is, then analyses of ecological patterns in terms of environmental factors can produce very misleading results. This is demonstrated using synthetic but realistic spatial patterns with known spatial properties which are subjected to classical correlation and multiple regression analyses. Correlation between an autocorrelated response variable and each of a set of explanatory variables is strongly biased in favour of those explanatory variables that are highly autocorrelated - the expected magnitude of the correlation coefficient increases with autocorrelation even if the spatial patterns are completely independent. Similarly, multiple regression analysis finds highly autocorrelated explanatory variables "significant" much more frequently than it should. The chances of mistakenly identifying a "significant" slope across an autocorrelated pattern is very high if classical regression is used. Consequently, under these circumstances strongly autocorrelated environmental factors reported in the literature as associated with ecological patterns may not actually be significant. It is likely that these factors wrongly described as important constitute a red-shifted subset of the set of potential explanations, and that more spatially discontinuous factors (those with bluer spectra) are actually relatively more important than their present status suggests. There is much that ecologists can do to improve on this situation. I discuss various approaches to the problem of spatial autocorrelation from the literature and present a randomisation test for the association of two spatial patterns which has advantages over currently available methods.     

Do landscape processes predict phylogeographic patterns in the wood frog?

Understanding factors that influence population connectivity and the spatial distribution of genetic variation is a major goal in molecular ecology. Improvements in the availability of high-resolution geographic data have made it increasingly possible to quantify the effects of landscape features on dispersal and genetic structure. However, most studies examining such landscape effects have been conducted at very fine (e.g. landscape genetics) or broad (e.g. phylogeography) spatial scales. Thus, the extent to which processes operating at fine spatial scales are linked to patterns at larger scales remains unclear. Here, we test whether factors impacting wood frog dispersal at fine spatial scales are correlated with genetic structure at regional scales. Using recently developed methods borrowed from electrical circuit theory, we generated landscape resistance matrices among wood frog populations in eastern North America based on slope, a wetness index, land cover and absolute barriers to wood frog dispersal. We then determined whether these matrices are correlated with genetic structure based on six microsatellite markers and whether such correlations outperform a landscape-free model of isolation by resistance. We observed significant genetic structure at regional spatial scales. However, topography and landscape variables associated with the intervening habitat between sites provide little explanation for patterns of genetic structure. Instead, absolute dispersal barriers appear to be the best predictor of regional genetic structure in this species. Our results suggest that landscape variables that influence dispersal, microhabitat selection and population structure at fine spatial scales do not necessarily explain patterns of genetic structure at broader scales.     

PEMPHIGUS REVISITED: CHANGES IN GEOGRAPHIC VARIATION BUT CONSTANCY IN VARIABILITY AND COVARIATION

Samples of the gall-forming aphids Pemphigus populicaulis and P. populitransversus (both elongate and globular morphs) were re-collected at sites in eastern North America after 13 to 16 years. Twenty-three morphometric characters of the galls, stem mothers, and alate fundatrigeniae were analyzed by univariate and multivariate methods. Varying proportions of the variance of each character are attributable to the four levels of variation—locality, year, year by locality interaction, and among galls (within one year and locality). The year by locality interaction level generally has the greatest variation and is highly significant. Year and locality effects tend to be lower and not significant. The variance components do not exhibit trends with time. Geographic variation patterns of single variables or factor scores in the original and revisited populations show significant spatial structure overall but lack clear-cut spatial patterns, especially clines. Observable patterns of variation match results of the spatial analyses: most characters lack clear trends; patterns in the revisited data do not resemble patterns for the same variables in the original data. Variability profiles for characters change little over the time span and are comparable among and within localities. Covariation among characters over localities is largely maintained during the time interval despite the changes in patterns. Fluctuating interclonal competition among aphids on secondary hosts is believed to cause the marked heterogeneity in space and time among the aphids in the galls.     

Spatial analysis of nuclear and mitochondrial RFLP genotypes in populations of the chestnut blight fungus, Cryphonectria parasitica

Spatial structure of both nuclear and mitochondrial RFLPs were studied in several populations of the chestnut blight fungus, Cryphonectria parasitica, using a variety of spatial autocorrelation tests designed to detect nonrandom patterns. Fungal individuals were sampled from cankers on infected chestnut trees, and the location of each tree was mapped. Single-locus nuclear RFLPs, nuclear fingerprints, and mitochondrial DNA haplotypes were determined for each individual. Individuals with the same DNA fingerprint genotypes occurred closer together than would be expected at random in four of the five plots, while mitochondrial DNA haplotypes were aggregated in all five plots. Genetic distances between individuals, expressed as one minus the proportion of shared restriction fragment size classes for fingerprints and mitochondrial haplotypes, were significantly correlated with Euclidean distances between individuals in four of the five populations, but these correlations were very weak (r < 0.18). The same DNA fingerprint and single-copy nuclear RFLP alleles occurred on the same trees or immediately neighbouring trees more often than would be expected at random. Most of the aggregation for all three genetic markers occurred among individuals within the same cluster of chestnut stems or on neighbouring trees. Lack of spatial autocorrelation in one population was probably due to sampling on a larger scale that was too coarse to detect any patterns. Significant aggregation of genotypes in C. parasitica is most likely caused by some degree of restricted dispersal within populations. The implications of restricted dispersal are discussed in relation to the breeding system and isolation by distance in populations of. C. parasitica.     

GENE DISPERSAL AND SPATIAL GENETIC STRUCTURE

Spatial autocorrelation statistics have been studied in theoretical population genetic models and widely used in experimental studies of spatial structure in many plant and animal populations. However, the statistical properties of spatial autocorrelation statistics have remained uncharacterized. Little is known about how values of spatial autocorrelation statistics in population samples depend on the level of dispersal and scheme of sampling. In this paper, we characterize the statistical properties of join-count spatial autocorrelation statistics for population genetic surveys under various conditions of dispersal and sampling. The results indicate generally high statistical power. These results can provide a method to estimate gene dispersal based on standing spatial patterns of genetic variation observed within populations.     

东北虎豹国家公园梅花鹿的空间遗传格局及其影响因素

东北梅花鹿是东北虎豹国家公园主要的大型食草动物之一,是东北虎豹的主要猎物,对针阔混交林群落的维持有关键的作用,探究其遗传多样性及空间遗传格局对东北梅花鹿的保护以及国家公园生态系统的健康至关重要。在国家公园珲春保护区内,通过非损伤方法获得遗传样本,利用微卫星标记,研究该梅花鹿种群的空间遗传格局及其影响因素。结果表明：本研究区梅花鹿种群平均期望杂合度为0.721,遗传多样性较为丰富。有限的扩散能力常常导致种群在遗传距离上具有显著的空间自相关模式,本研究区梅花鹿种群在0-1km距离等级内在遗传距离上具有显著的空间自相关现象,据此可推测,该地区梅花鹿扩散距离为1km左右。STRUCTURE分析表明,珲春地区梅花鹿种群不存在明显的遗传分化。各种空间变量可以显著影响物种的遗传分化。本研究选取海拔、坡度、坡向、地表起伏率、人类干扰5个变量,研究其对梅花鹿种群遗传结构的影响,这5个变量多被认为与大中型哺乳动物扩散阻碍相关。依据5个变量建立了336个阻力模型,并进行偏曼特尔检验。其中,依据海拔、坡向、地表起伏率、人类干扰假设建立的246个阻力模型与遗传距离之间的关系并不显著,综合所有变量的15个生境适宜性模型阻力模型与遗传距离的关系也都不显著。在依据坡度假设建构的75个阻力模型中,只有1个模型与遗传距离有显著的正相关关系,该模型同时也是在控制空间自相关影响后,在所有模型中与遗传距离相关性最高的模型。根据该模型推测,最适宜梅花鹿扩散的坡度为10°,梅花鹿可能倾向于利用缓坡进行扩散。结果对东北虎豹国家公园梅花鹿种群的保护具有重要意义。     

Detecting clinal and balanced selection using spatial autocorrelation analysis under kin-structured migration

Recently spatial autocorrelation has been employed to infer microevolutionary processes from patterns of genetic variation. In theory, different processes should show characteristic signature correlograms; e. g., clinal selection should produce correlograms decreasing from positive to negative autocorrelation, whereas uniform balanced selection should lead to no spatial autocorrelation. The ability of a statistical method such as spatial autocorrelation analysis to distinguish between these selective regimes or even to detect departures from neutrality is dependent on the strength of the evolutionary force and the population structure. Weak selection or migration will not be apparent against the expected background of stochastic noise. Moreover, the population structure may generate sufficient stochastic variation such that even strong evolutionary forces may fail to be detected. This study uses computer simulation to examine the effects of kin-structured migration and three different selective regimes on the shape of spatial correlograms to assess the ability of this technique to detect different microevolutionary processes. Genetic variation among 8 loci is simulated in a linear set of 25 artificial populations. Kin-structured stepping-stone migration among adjacent populations is modeled; directional, balanced, and clinal selection, as well as neutral loci are considered. These experiments show that strong selection produces correlograms of the predicted shape. However, with an anthropologically reasonable population structure, considerable stochastic variation among correlograms for different alleles may still exist. This suggests the need for caution in inferring genetic process from spatial patterns. © 1994 Wiley-Liss, Inc.     

Population structure in a common Caribbean coral-reef fish: implications for larval dispersal and early life-history traits

Genetic population structure throughout the Caribbean Basin for one of the most common and widespread reef fish species, the bicolour damselfish Stegastes partitus was examined using microsatellite DNA markers. Spatial autocorrelation analysis showed a significant positive correlation between genetic and geographic distance (isolation by distance) over distances <1000 km, suggesting that populations are connected genetically but probably not demographically, i.e. over shorter time scales. A difference in spatial patterns of populations in the eastern v. the western Caribbean also raises the probability of an important role for meso-scale oceanographic features and landscape complexity within the same species. A comparison of S. partitus population structure and life-history traits with those of two other species of Caribbean reef fish studied earlier showed the findings to be concordant with a common hypothesis that shorter pelagic larval dispersal periods are associated with smaller larval dispersal scales.     

Spatial scaling and transition in pneumatophore arthropod communities

Although most ecological variables are scale-dependent, few studies cover a broad range of spatial scales. Here, we consider South African mangrove pneumatophore arthropod communities (mites, crustaceans and insects), across seven spatial scales (from 10  cm to 100  km). We plot spatial autocorrelation in individual species, evaluate if resource and habitat availability determine spatial patterning, and identify the scales of community transition. Spatial autocorrelation in most ecological variables decreased with increasing spatial scale, with notable exceptions for the larger scales. Negative abundance autocorrelation was stronger at 10  km than at 100  km for common species, while the opposite was true for rare species. Spatial autocorrelation in species richness decreased from 1  m (strong positive) to 10  km (strong negative), but was not significant at the 100  km scale. These patterns reflect the patchy distribution of pneumatophores within mangrove forests, that of the forests along the coast, and the poor dispersal abilities of most of the arthropods sampled, in a highly dynamic environment. Although resource and habitat availability exhibited a similar autocorrelation pattern to that of the community, the total mass of pneumatophores did not appear to be an important determinant of community structure. Variations in the abundance of common species, as well as the restricted distribution of rare species caused assemblage structure to change gradually with increasing distance from 10 cm to 100 km, but only marginally from 10 to 100  km. We highlight the need for cross-scale studies in bridging the gap between two key ecological concepts: potential ecological niche and realized geographic range.     

Environmental colour intensifies the Moran effect when population dynamics are spatially heterogeneous

Evidence for synchronous fluctuations of spatially separated populations is ubiquitous in the literature, including accounts within and across taxa. Among the few mechanisms explaining this phenomenon is the Moran effect, whereby independent populations are synchronized by spatially correlated environmental disturbances. The body of research on the Moran effect predominantly assumes that environmental disturbances within a local site are serially uncorrelated; that is, successive observations in time at a particular local site are independent. Yet, many environmental variables are known to possess strong temporal autocorrelation – a character which has often been described as 'colour'. The omission of environmental colour from research on the Moran effect may be due in part to the lack of methods capable of generating sets of time series with a desired colour and spatial correlation. Here I present a novel and simple method designated as 'phase partnering' to generate such sets of time series and I investigate the combined impact of spatial correlation and environmental colour on population synchrony in two common models of population dynamics. For linear population dynamics, and for a subset of nonlinear population dynamics, coloured environments intensify the Moran effect when population dynamics are spatially heterogeneous; in coloured environments the spatial correlation between populations more closely mimics the spatial correlation between their respective environments. Given that most environmental variables are coloured, these results imply that the Moran effect may be a far more significant driver of regional-scale population and interspecific synchrony than is currently believed.     

Developmental perspectives on neurocranial proportions in Japan

This study employs juvenile cranial data derived from collections dating to between about 5000 years ago and the present in order to investigate how differences in cranial growth trajectories contributed to inter-group variation in cranial shape among temporally defined Japanese populations. As gene influx from the Asian mainland was insignificant after the Yayoi period (c. CE 300), differences in adult cranial shape among later assemblages from Japan are probably related to developmental adaptations to environmental change. Comparing cranial growth trajectories among groups from different time periods allows indirect testing of several hypotheses about secular changes in cranial growth, including thermoregulatory adaptation, change in levels of masticatory stress, and change in levels of physiological stress.Differences in neurocranial proportions among groups with contrasting adult cranial shapes were found to be already pronounced for the infant cohort (0-3 years of age) and actually tended to decline slightly within later age ranges; differences in mandibular shape were unremarkable early in life, but became more pronounced after infancy. Consequently, changes in chewing stress are unlikely to have been the principal factor driving inter-group differences in cranial proportions. The cranial growth pattern reconstructed from a Medieval Japanese skeletal series showed the greatest magnitude of difference from those reconstructed for other time periods. Unlike in the other groups, there was no marked decline of cephalic index with age for the Medieval series. The unusual trajectory of cranial growth evident in the Medieval sample may result from a high degree of physiological stress due to overall poor nutrition.     

Assessing spatial covariance among time series of abundance

For species of conservation concern, an essential part of the recovery planning process is identifying discrete population units and their location with respect to one another. A common feature among geographically proximate populations is that the number of organisms tends to covary through time as a consequence of similar responses to exogenous influences. In turn, high covariation among populations can threaten the persistence of the larger metapopulation. Historically, explorations of the covariance in population size of species with many (>10) time series have been computationally difficult. Here, we illustrate how dynamic factor analysis (DFA) can be used to characterize diversity among time series of population abundances and the degree to which all populations can be represented by a few common signals. Our application focuses on anadromous Chinook salmon (Oncorhynchus tshawytscha), a species listed under the US Endangered Species Act, that is impacted by a variety of natural and anthropogenic factors. Specifically, we fit DFA models to 24 time series of population abundance and used model selection to identify the minimum number of latent variables that explained the most temporal variation after accounting for the effects of environmental covariates. We found support for grouping the time series according to 5 common latent variables. The top model included two covariates: the Pacific Decadal Oscillation in spring and summer. The assignment of populations to the latent variables matched the currently established population structure at a broad spatial scale. At a finer scale, there was more population grouping complexity. Some relatively distant populations were grouped together, and some relatively close populations – considered to be more aligned with each other – were more associated with populations further away. These coarse‐ and fine‐grained examinations of spatial structure are important because they reveal different structural patterns not evident in other analyses.