Hot Spots, Cold Spots, and the Geographic Mosaic Theory of Coevolution

Species interactions commonly coevolve as complex geographic mosaics of populations shaped by differences in local selection and gene flow. We use a haploid matching-alleles model for coevolution to evaluate how a pair of species coevolves when fitness interactions are reciprocal in some locations ("hot spots") but not in others ("cold spots"). Our analyses consider mutualistic and antagonistic interspecific interactions and a variety of gene flow patterns between hot and cold spots. We found that hot and cold spots together with gene flow influence coevolutionary dynamics in four important ways. First, hot spots need not be ubiquitous to have a global influence on evolution, although rare hot spots will not have a disproportionate impact unless selection is relatively strong there. Second, asymmetries in gene flow can influence local adaptation, sometimes creating stable equilibria at which species experience minimal fitness in hot spots and maximal fitness in cold spots, or vice versa. Third, asymmetries in gene flow are no more important than asymmetries in population regulation for determining the maintenance of local polymorphisms through coevolution. Fourth, intraspecific allele frequency differences among hot and cold spot populations evolve under some, but not all, conditions. That is, selection mosaics are indeed capable of producing spatially variable coevolutionary outcomes across the landscapes over which species interact. Altogether, our analyses indicate that coevolutionary trajectories can be strongly shaped by the geographic distribution of coevolutionary hot and cold spots, and by the pattern of gene flow among populations.     

Coevolution between hosts and parasites with partially overlapping geographic ranges

Many host species interact with a specific parasite within only a fraction of their geographical range. Where host and parasite overlap geographically, selection may be reciprocal constituting a coevolutionary hot spot. Host evolution, however, may be driven primarily by selection imposed by alternative biotic or abiotic factors that occur outside such hot spots. To evaluate the importance of coevolutionary hot spots for host and parasite evolution, we analyse a spatially explicit genetic model for a host that overlaps with a parasite in only part of its geographical range. Our results show that there is a critical amount of overlap beyond which reciprocal selection leads to a coevolutionary response in the host. This critical amount of overlap depends upon the explicit spatial configuration of hot spots. When the amount of overlap exceeds this first critical level, host-parasite coevolution commonly generates stable allele frequency clines rather than oscillations. It is within this region that one of the primary predictions of the geographic mosaic theory is realized, and local maladaptation is prevalent in both species. Past a further threshold of overlap between the species oscillations do evolve, but allele frequencies in both species are spatially synchronous and local maladaptation is absent in both species. A consequence of such transitions between coevolutionary dynamics is that parasite adaptation is inversely proportional to the fraction of its host's range that it occupies. Hence, as the geographical range of a parasite increases, it becomes increasingly maladapted to the host. This suggests a novel mechanism through which the geographical range of parasites may be limited.     

基于ESDA的西北太平洋柔鱼资源空间热点区域及其变动研究

以西北太平洋(150°E—160°E、38°N—45°N)柔鱼Ommastrephes bartramii为研究对象,以2007和2010年鱿钓渔业的原始点位数据为基础,利用常规统计和探索性空间数据分析(ESDA)中的全局空间自相关分析方法,对西北太平洋柔鱼资源空间分布及其变动进行了研究。结果表明,西北太平洋柔鱼资源呈现较强的聚集分布特征,但不同空间位置差异较大。利用GIS和ESDA的局部自相关方法进行了柔鱼资源热冷点空间分布的制图,结果显示2007年研究区存在3个显著的热点和1个冷点,2010年与之存在较为明显的差异,具有1个热点和4个冷点。变化检测分析表明,两个年份间出现1个热点区域和1个冷点区域未发生变化,另有2个区域从2007年热点变成2010年冷点;此外,非热冷点之间的变动在研究区占据了主导地位。景观指数分析表明,研究区柔鱼资源热冷点格局的复杂性和自相似性并不高,但其异质性和聚集性非常强,且整体性和凝聚度均很高。分析认为,2007年西北太平洋柔鱼渔区的形成受温度和海流的影响,近一半作业渔区产量相对较高,但并未充分保证空间热点的大面积形成,空间热点和冷点分布面积大致相当;2010年整个西北太平洋柔鱼渔场受亲潮势力影响,空间热点较为集中、空间冷点较多且分散。     

Spatially autocorrelated sampling falsely inflates measures of accuracy for presence‐only niche models

Aim Environmental niche models that utilize presence‐only data have been increasingly employed to model species distributions and test ecological and evolutionary predictions. The ideal method for evaluating the accuracy of a niche model is to train a model with one dataset and then test model predictions against an independent dataset. However, a truly independent dataset is often not available, and instead random subsets of the total data are used for ‘training’ and ‘testing’ purposes. The goal of this study was to determine how spatially autocorrelated sampling affects measures of niche model accuracy when using subsets of a larger dataset for accuracy evaluation. Location The distribution of Centaurea maculosa (spotted knapweed; Asteraceae) was modelled in six states in the western United States: California, Oregon, Washington, Idaho, Wyoming and Montana. Methods Two types of niche modelling algorithms – the genetic algorithm for rule‐set prediction (GARP) and maximum entropy modelling (as implemented with Maxent) – were used to model the potential distribution of C. maculosa across the region. The effect of spatially autocorrelated sampling was examined by applying a spatial filter to the presence‐only data (to reduce autocorrelation) and then comparing predictions made using the spatial filter with those using a random subset of the data, equal in sample size to the filtered data. Results The accuracy of predictions from both algorithms was sensitive to the spatial autocorrelation of sampling effort in the occurrence data. Spatial filtering led to lower values of the area under the receiver operating characteristic curve plot but higher similarity statistic (I) values when compared with predictions from models built with random subsets of the total data, meaning that spatial autocorrelation of sampling effort between training and test data led to inflated measures of accuracy. Main conclusions The findings indicate that care should be taken when interpreting the results from presence‐only niche models when training and test data have been randomly partitioned but occurrence data were non‐randomly sampled (in a spatially autocorrelated manner). The higher accuracies obtained without the spatial filter are a result of spatial autocorrelation of sampling effort between training and test data inflating measures of prediction accuracy. If independently surveyed data for testing predictions are unavailable, then it may be necessary to explicitly account for the spatial autocorrelation of sampling effort between randomly partitioned training and test subsets when evaluating niche model predictions.     

Spatial autocorrelation as a tool for identifying the geographical patterns of aphid annual abundance

Abstract 1 A spatial autocorrelation analysis was undertaken to investigate the spatial structure of annual abundance for the pest aphid Myzus persicae collected in suction traps distributed across north‐west Europe. 2 The analysis was applied at two different scales. The Moran index was used to estimate the degree of spatial autocorrelation at all sites within the study area (global level). The contributions of each site to the global index were identified by the use of a local indicator of spatial autocorrelation (LISA). A hierarchical cluster analysis was undertaken to highlight differences between groups of resulting correlograms. 3 Similarity between traps was shown to occur over large geographical distances, suggesting an impact of phenomena such as climatic gradients or land use types. 4 The presence of outliers and zones of similarity (hot‐spots) and of dissimilarity (cold‐spots) were identified indicating a strong impact of local effects. 5 Several groups of traps characterized by similarities in their local spatial structure (correlograms, value of Moran's I_i) also had similar values for land use variables (the area occupied by agricultural zones, forest and sea). 6 It is concluded that trap data can provide information about Myzus persicae that is representative of large geographical areas. Thus, trap data can be used to estimate the aerial abundance of this species, even if the suction traps are not regularly and densely distributed.     

Scale-related patterns in the spatial and environmental components of stream macroinvertebrate assemblage variation

Aim We examined the relative contributions of spatial gradients and local environmental conditions to macroinvertebrate assemblages of boreal headwater streams at three hierarchical extents: bioregion, ecoregion and drainage system. We also aimed to identify the environmental variables most strongly related to assemblage structure at each study scale, and to assess how the importance of these variables is related to regional context and spatial structuring at different scales. Location Northern Finland ( 62 – 68° N, 25–32° E). Methods Variation in macroinvertebrate data was partitioned using partial canonical correspondence analysis into components explained by spatial variables (nine terms from the cubic trend surface regression), local environmental variables (15 variables) and spatially structured environmental variation. Results The strength of the relationship between assemblage structure and local environmental variables increased with decreasing spatial extent, whereas assemblage variation related to spatial variables and spatially structured environmental variation showed the opposite pattern. At the largest extents, spatial variation was related to latitudinal gradients, whereas spatial autocorrelation among neighbouring streams was the likely mechanism creating spatial structure within drainage systems. Only stream size and water acidity were consistently important in explaining assemblage structure at all study scales, while the importance of other environmental variables was more context‐dependent. Main conclusions The importance of local environmental factors in explaining macroinvertebrate assemblage structure increases with decreasing spatial extent. This scale‐related pattern is not caused solely by changes in study extent, however, but also by variable sample sizes at different regional extents. The importance of environmental gradients is context‐dependent and few factors are likely to be universally important correlates of macroinvertebrate assemblage structure. Finally, our results suggest that bioassessment should give due attention to spatial structuring of stream assemblages, because important assemblage gradients may not only be related to local factors but also to biogeographical constraints and neighbourhood dispersal processes.     

Size of environmental grain and resource matching

For most animals their foraging environment consists of a patch network. In random environments there are no spatial autocorrelation at all, while in fine-grained systems positive autocorrelations flip to negative ones and back again against distance. With increasing grain size the turnover rate of spatial autocorrelation slows down. Using a cellular automaton with foragers having limited information about their feeding environment we examined how well consumer numbers matched resource availability, also known as the ideal free distribution. The match is the better the smaller the size of the environmental grain. This is somewhat contrary to the observation that in large-grained environments the spatial autocorrelation is high and positive over long distances. In such an environment foragers, by knowing a limited surrounding, should in fact know a much larger area because of the spatially autocorrelated resource pattern. Yet, when foragers have limited knowledge, we observed that the degree of undermatching (i.e., more individuals in less productive patches than expected) increases with increasing grain size.     

Natural meiotic recombination hot spots in the Schizosaccharomyces pombe genome successfully predicted from the simple sequence motif M26

The M26 hot spot of meiotic recombination in Schizosaccharomyces pombe is the eukaryotic hot spot most thoroughly investigated at the nucleotide level. The minimum sequence required for M26 activity was previously determined to be 5'-ATGACGT-3'. Originally identified by a mutant allele, ade6-M26, the M26 heptamer sequence occurs in the wild-type S. pombe genome approximately 300 times, but it has been unclear whether any of these are active hot spots. Recently, we showed that the M26 heptamer forms part of a larger consensus sequence, which is significantly more active than the heptamer alone. We used this expanded sequence as a guide to identify a smaller number of sites most likely to be active hot spots. Ten of the 15 sites tested showed meiotic DNA breaks, a hallmark of recombination hot spots, within 1 kb of the M26 sequence. Among those 10 sites, one occurred within a gene, cds1(+), and hot spot activity of this site was confirmed genetically. These results are, to our knowledge, the first demonstration in any organism of a simple, defined nucleotide sequence accurately predicting the locations of natural meiotic recombination hot spots. M26 may be the first example among a diverse group of simple sequences that determine the distribution, and hence predictability, of meiotic recombination hot spots in eukaryotic genomes.     

中西太平洋鲣鱼围网渔业资源的热点分析和空间异质性

中西太平洋是世界鲣鱼围网主要作业水域。基于我国渔船2005—2009年的中西太平洋鲣鱼围网生产数据,运用空间统计方法对该水域鲣鱼资源的空间自相关性和空间异质性特征进行分析,并结合海洋环境特征分析资源分布的热点区域。(1)通过常规统计学计算获得鲣鱼资源的偏态Sk、峰态数Ku、变异值Cv、s2/m和全局空间自相关Geary c系数,发现中西太平洋鲣鱼资源总体上是以低密度区域为主,高密度区域较少;鱼类资源密度值差异较大,资源表现出强烈集聚分布,总体的空间自相关性中等偏弱。(2)通过局部空间自相关的热点分析方法计算,发现局部空间自相关性较强,存在多个在统计学上通过显著性检验的资源热点和冷点。(3)通过地统计方法研究鲣鱼资源的空间变异性特征和方向变异时,空间自相关类型上最优模型是球形模型,鲣鱼资源密度各向同性,最大相关距离1000km左右。发现空间自相关引起的差异占整个差异的50%左右,为中等强度变异;在方向性变异上,主要体现在南北向上,其该向上结构性误差占67%,而东西向结构性误差占49%。这一结果和海洋环境的南北向上结构性远好于东西向结构性有关;从各方向的分维数看,数值介于1.876—1.9之间,数值较大,空间自相关较弱。(4)以资源热点区域作为区域性渔场,结合海洋温度和叶绿素场海洋环境特征,将中西太平洋鲣鱼资源分为3个不同的局部渔场,即2个暖池渔场,1个冷舌渔场。冷舌渔场由中东太平洋赤道上升流引起,在锋面地带提供了较为丰富的初级生产力,便于鱼类获得丰富的食物;暖池渔场靠近岛屿和陆地区域,近岸上升流系统提供了丰富的初级生产力。(5)将热点分析和渔场重心方法及栖息地指数的优缺点做了对比,建议以后采用空间残差模型深入研究空间自相关问题。     

Hot Spots of Inorganic Nitrogen Availability in an Alpine-Subalpine Ecosystem,Colorado Front Range

Inorganic nitrogen (N) availability hot spots have been documented in many ecosystems, but major uncertainties remain about their prevalence, timing, and causes. Using a novel mathematical definition of hot spots, spatially explicit measurements of KCl-extractable inorganic N, 2-week soil incubations in the field, ion-exchange resins deployed for 1 year, and a set of associated biotic and abiotic variables, we investigated inorganic N availability hot spots within a 0.89 km² alpine-subalpine ecosystem in the Colorado Front Range. Measurements of KCl-extractable NH₄ ⁺ and NO₃ ⁻ taken on multiple dates showed that hot spots of N availability were present in some but not all parts of the study site and that hot spot location varied over the course of the season. Ion-exchange resins showed that over a 1-year period hot spots were important contributors to resin-available N at the landscape level, with 14% of resin locations accounting for 58% of total resin-extractable inorganic N. The KCl-extractable and resin-available inorganic N measurements showed that although spatial variation in the timing of hot spots (that is, hot moments) spreads the influence of short-term hot spots across the landscape to some extent, spatial variation in inorganic N availability is still important when integrated over 1 year. Resin-available N was poorly correlated with the biotic and abiotic variables that we measured, though we did observe that hot spots of resin-available N were twice as common below tree and shrub canopies than in herbaceous areas. Beyond this relationship with canopy structure, neither KCl-extractable nor resin-available inorganic N hot spots were closely related to plant species identity. Instead, the most effective predictor of KCl-extractable NH₄ ⁺ was the size of the soil organic matter (SOM) N pool, with nearly all hot spots appearing in soils that had greater than 1.4% SOM N.     

Protected and threatened components of fish biodiversity in the Mediterranean sea

The Mediterranean Sea (0.82% of the global oceanic surface) holds 4%-18% of all known marine species (~17,000), with a high proportion of endemism [1, 2]. This exceptional biodiversity is under severe threats [1] but benefits from a system of 100 marine protected areas (MPAs). Surprisingly, the spatial congruence of fish biodiversity hot spots with this MPA system and the areas of high fishing pressure has not been assessed. Moreover, evolutionary and functional breadth of species assemblages [3] has been largely overlooked in marine systems. Here we adopted a multifaceted approach to biodiversity by considering the species richness of total, endemic, and threatened coastal fish assemblages as well as their functional and phylogenetic diversity. We show that these fish biodiversity components are spatially mismatched. The MPA system covers a small surface of the Mediterranean (0.4%) and is spatially congruent with the hot spots of all taxonomic components of fish diversity. However, it misses hot spots of functional and phylogenetic diversity. In addition, hot spots of endemic species richness and phylogenetic diversity are spatially congruent with hot spots of fishery impact. Our results highlight that future conservation strategies and assessment efficiency of current reserve systems will need to be revisited after deconstructing the different components of biodiversity.     

Protein subunit interfaces: a statistical analysis of hot spots in Sm proteins

The distinguishing property of Sm protein associations is very high stability. In order to understand this property, we analyzed the interfaces and compared the properties of Sm protein interfaces with those of a test set, the Binding Interface Database (BID). The comparison revealed that the main differences between the interfaces of Sm proteins and those of the BID set are the content of charged residues, the coordination numbers of the residues, knowledge-based pair potentials, and the conservation scores of hot spots. In Sm proteins, the interfaces have more hydrophobic and fewer charged residues than the surfaces, which is also the case for the BID test set and other proteins. However, in the interfaces, the content of charged residues in Sm proteins (26%) is substantially larger than that in the BID set (22%). Hot spots are residues that make up a small fraction of the interfaces, but they contribute most of the binding energy. These residues are critical to protein–protein interactions. Analyses of knowledge-based pair potentials of hot spot and non-hot spot residues in Sm proteins show that they are significantly different; their mean values are 31.5 and 11.3, respectively. In the BID set, this difference is smaller; in this case, the mean values for hot spot and non-hot spot residues are 20.7 and 12.4, respectively. Hence, the pair potentials of hot spots differ significantly for the Sm and BID data sets. In the interfaces of Sm proteins, the amino acids are tightly packed, and the coordination numbers are larger in Sm proteins than in the BID set for both hot spots and non-hot spots. At the same time, the coordination numbers are higher for hot spots; the average coordination number of the hot spot residues in Sm proteins is 7.7, while it is 6.1 for the non-hot spot residues. The difference in the calculated average conservation score for hot spots and non-hot spots in Sm proteins is significantly larger than it is in the BID set. In Sm proteins, the average conservation score for the hot spots is 7.4. Hot spots are surrounded by residues that are moderately conserved (5.9). The average conservation score for the other interface residues is 5.6. The conservation scores in the BID set do not show a significant distinction between hot and non-hot spots: the mean values for hot and non-hot spot residues are 5.5 and 5.2, respectively. These data show that structurally conserved residues and hot spots are significantly correlated in Sm proteins.     

Fine-scale natal homing and localized movement as shaped by sex and spawning habitat in Chinook salmon: insights from spatial autocorrelation analysis of individual genotypes

Natal homing is a hallmark of the life history of salmonid fishes, but the spatial scale of homing within local, naturally reproducing salmon populations is still poorly understood. Accurate homing (paired with restricted movement) should lead to the existence of fine-scale genetic structuring due to the spatial clustering of related individuals on spawning grounds. Thus, we explored the spatial resolution of natal homing using genetic associations among individual Chinook salmon (Oncorhynchus tshawytscha) in an interconnected stream network. We also investigated the relationship between genetic patterns and two factors hypothesized to influence natal homing and localized movements at finer scales in this species, localized patterns in the distribution of spawning gravels and sex. Spatial autocorrelation analyses showed that spawning locations in both sub-basins of our study site were spatially clumped, but the upper sub-basin generally had a larger spatial extent and continuity of redd locations than the lower sub-basin, where the distribution of redds and associated habitat conditions were more patchy. Male genotypes were not autocorrelated at any spatial scale in either sub-basin. Female genotypes showed significant spatial autocorrelation and genetic patterns for females varied in the direction predicted between the two sub-basins, with much stronger autocorrelation in the sub-basin with less continuity in spawning gravels. The patterns observed here support predictions about differential constraints and breeding tactics between the two sexes and the potential for fine-scale habitat structure to influence the precision of natal homing and localized movements of individual Chinook salmon on their breeding grounds.     

Predicting Hot Spots of Herpetofauna Road Mortality Along Highway Networks

ABSTRACT Road mortality is often spatially aggregated, and there is a need for models that accurately and efficiently predict hot spots within a road network for mitigation. We surveyed 145 points throughout a 353-km highway network in New York State, USA, for roadkill of reptiles and amphibians. We used land cover, wetland configuration, and traffic volume data to identify features that best predicted hot spots of herpetofauna road mortality. We resampled 40 points an additional 4 times over 4 years to evaluate temporal repeatability. Both amphibian and reptile road mortality were spatially clustered, and road-kill hot spots of the 2 taxa overlapped. One survey provided a valid snapshot of spatial patterns of road mortality, and spatial patterns remained stable across time. Road-kill hot spots were located where wetlands approached within 100 m of the road, and the best predictor was a causeway configuration of wetlands (wetlands on both sides of the road). We validated causeways as predictors of road mortality by surveying 180 causeways and 180 random points across 5 regions (17,823 km²) of northeastern New York. Causeways were 3 times more likely than random locations to have amphibian and 12 times more likely to have reptile mortality present, and causeways had a 4 times higher total number of amphibian roadkill and 9 times higher reptile roadkill than did random points. We conclude it is possible to identify valid predictors of hot spots of amphibian and reptile road mortality for use when planning roads or when conducting surveys on existing roads to locate priority areas for mitigation.     

Variance and spatial scales in a tropical rain forest: changing the size of sampling units

The size of a sampling unit has a critical effect on our perception of ecological phenomena; it influences the variance and correlation structure estimates of the data. Classical statistical theory works well to predict the changes in variance when there is no autocorrelation structure, but it is not applicable when the data are spatially autocorrelated. Geostatistical theory, on the other hand, uses analytical relationships to predict the variance and autocorrelation structure that would be observed if a survey was conducted using sampling units of a different size. To test the geostatistical predictions, we used information about individual tree locations in the tropical rain forest of the Pasoh Reserve, Malaysia. This allowed us to simulate and compare various sampling designs. The original data were reorganised into three artificial data sets, computing tree densities (number of trees per square meter in each quadrat) corresponding to three quadrat sizes (5×5, 10×10 and 20×20 m⁽²⁾). Based upon the 5×5 m⁽²⁾ data set, the spatial structure was modelled using a random component (nugget effect) plus an exponential model for the spatially structured component. Using the within-quadrat variances inferred from the variogram model, the change of support relationships predicted the spatial autocorrelation structure and new variances corresponding to 10×10 m⁽²⁾ and 20×20 m⁽²⁾ quadrats. The theoretical and empirical results agreed closely, while the classical approach would have largely underestimated the variance. As quadrat size increases, the range of the autocorrelation model increases, while the variance and proportion of noise in the data decrease. Large quadrats filter out the spatial variation occurring at scales smaller than the size of their sampling units, thus increasing the proportion of spatially structured component with range larger than the size of the sampling units.     

A Role of Tyrosine Phosphatase in Acetylcholine Receptor Cluster Dispersal and Formation

Innervation of the skeletal muscle involves local signaling, leading to acetylcholine receptor (AChR) clustering, and global signaling, manifested by the dispersal of preexisting AChR clusters (hot spots). Receptor tyrosine kinase (RTK) activation has been shown to mediate AChR clustering. In this study, the role of tyrosine phosphatase (PTPase) in the dispersal of hot spots was examined. Hot spot dispersal in cultured Xenopus muscle cells was initiated immediately upon the presentation of growth factor–coated beads that induce both AChR cluster formation and dispersal. Whereas the density of AChRs decreased with time, the fine structure of the hot spot remained relatively constant. Although AChR, rapsyn, and phosphotyrosine disappeared, a large part of the original hot spot–associated cytoskeleton remained. This suggests that the dispersal involves the removal of a key linkage between the receptor and its cytoskeletal infrastructure. The rate of hot spot dispersal is inversely related to its distance from the site of synaptic stimulation, implicating the diffusible nature of the signal. PTPase inhibitors, such as pervanadate or phenylarsine oxide, inhibited hot spot dispersal. In addition, they also affected the formation of new clusters in such a way that AChR microclusters extended beyond the boundary set by the clustering stimuli. Furthermore, by introducing a constitutively active PTPase into cultured muscle cells, hot spots were dispersed in a stimulus- independent fashion. This effect of exogenous PTPase was also blocked by pervanadate. These results implicate a role of PTPase in AChR cluster dispersal and formation. In addition to RTK activation, synaptic stimulation may also activate PTPase which acts globally to destabilize preexisting AChR hot spots and locally to facilitate AChR clustering in a spatially discrete manner by countering the action of RTKs.     

基于夜间灯光数据的湖南省县域碳排放时空格局及影响因素研究

研究碳排放的时空格局演变及影响因素对指导制定差异化的碳减排政策具有重要意义。基于夜间灯光数据,在估算湖南省各县区碳排放量的基础上,结合空间统计、空间自相关、热点分析、地理加权回归、GIS等方法研究了湖南省县域碳排放的空间分异、时空格局特征与影响因素。研究结果表明:(1)2013—2017年,湖南省能源消费碳排放总体上呈现东高西低的空间格局,碳排放主要集中于区域的市辖区,县域碳排放最高点在长沙市市辖区;(2)湖南省能源消费碳排放存在较为显著的空间正相关,全省县域尺度能源消费碳排放全局Moran’sI指数整体呈现出逐年上升的趋势,各市的市辖区在中心相互辐射,表现出显著的集聚现象,并形成了碳排放“高-高型”分布特征;(3)湖南省能源消费碳排放量的冷热点格局表现出湘南地区冷点扩张,湘中地区热点扩张的演变趋势,从2013年到2017年,热点区与次热点区由11个升至13个,湘中地区与其他地区的冷热点差距在逐步拉大;(4)影响湖南省县域能源消费碳排放量的4个影响因素与碳排放均表现为正相关性,其影响程度依次为人口、人均GDP、第二产业比重与单位GDP能耗。     

Examining the spatial distribution of flower thrips in southern highbush blueberries by utilizing geostatistical methods

Flower thrips (Frankliniella spp.) are one of the key pests of southern highbush blueberries (Vaccinium corymbosum L. x V. darrowii Camp), a high-value crop in Florida. Thrips' feeding and oviposition injury to flowers can result in fruit scarring that renders the fruit unmarketable. Flower thrips often form areas of high population, termed "hot spots", in blueberry plantings. The objective of this study was to model thrips spatial distribution patterns with geostatistical techniques. Semivariogram models were used to determine optimum trap spacing and two commonly used interpolation methods, inverse distance weighting (IDW) and ordinary kriging (OK), were compared for their ability to model thrips spatial patterns. The experimental design consisted of a grid of 100 white sticky traps spaced at 15.24-m and 7.61-m intervals in 2008 and 2009, respectively. Thirty additional traps were placed randomly throughout the sampling area to collect information on distances shorter than the grid spacing. The semivariogram analysis indicated that, in most cases, spacing traps at least 28.8 m apart would result in spatially independent samples. Also, the 7.61-m grid spacing captured more of the thrips spatial variability than the 15.24-m grid spacing. IDW and OK produced maps with similar accuracy in both years, which indicates that thrips spatial distribution patterns, including "hot spots," can be modeled using either interpolation method. Future studies can use this information to determine if the formation of "hot spots" can be predicted using flower density, temperature, and other environmental factors. If so, this development would allow growers to spot treat the "hot spots" rather than their entire field.     

Spatially structured metapopulation models: global and local assessment of metapopulation capacity

We model metapopulation dynamics in finite networks of discrete habitat patches with given areas and spatial locations. We define and analyze two simple and ecologically intuitive measures of the capacity of the habitat patch network to support a viable metapopulation. Metapopulation persistence capacity lambda(M) defines the threshold condition for long-term metapopulation persistence as lambda(M)>delta, where delta is defined by the extinction and colonization rate parameters of the focal species. Metapopulation invasion capacity lambda(I) sets the condition for successful invasion of an empty network from one small local population as lambda(I)>delta. The metapopulation capacities lambda(M) and lambda(I) are defined as the leading eigenvalue or a comparable quantity of an appropriate "landscape" matrix. Based on these definitions, we present a classification of a very general class of deterministic, continuous-time and discrete-time metapopulation models. Two specific models are analyzed in greater detail: a spatially realistic version of the continuous-time Levins model and the discrete-time incidence function model with propagule size-dependent colonization rate and a rescue effect. In both models we assume that the extinction rate increases with decreasing patch area and that the colonization rate increases with patch connectivity. In the spatially realistic Levins model, the two types of metapopulation capacities coincide, whereas the incidence function model possesses a strong Allee effect characterized by lambda(I)=0. For these two models, we show that the metapopulation capacities can be considered as simple sums of contributions from individual habitat patches, given by the elements of the leading eigenvector or comparable quantities. We may therefore assess the significance of particular habitat patches, including new patches that might be added to the network, for the metapopulation capacities of the network as a whole. We derive useful approximations for both the threshold conditions and the equilibrium states in the two models. The metapopulation capacities and the measures of the dynamic significance of particular patches can be calculated for real patch networks for applications in metapopulation ecology, landscape ecology, and conservation biology.     

Spind: a package for computing spatially corrected accuracy measures

Using an appropriate accuracy measure is essential for assessing prediction accuracy in species distribution modelling. Therefore, model evaluation as an analytical uncertainty is a challenging problem. Although a variety of accuracy measures for the assessment of prediction errors in presence/absence models is available, there is a lack of spatial accuracy measures, i.e. measures that are sensitive to the spatial arrangement of the predictions. We present ‘spind’, a new software package (based on the R software program) that provides spatial performance measures for grid‐based models. These accuracy measures are generalized, spatially corrected versions of the classical ones, thus enabling comparisons between them. Our method for evaluation consists of the following steps: 1) incorporate additional autocorrelation until spatial autocorrelation in predictions and actuals is balanced, 2) cross‐classify predictions and adjusted actuals in a 4 × 4 contingency table, 3) use a refined weighting pattern for errors, and 4) calculate weighted Kappa, sensitivity, specificity and subsequently ROC, AUC, TSS to get spatially corrected indices. To illustrate the impact of our spatial method we present an example of simulated data as well as an example of presence/absence data of the plant species Dianthus carthusianorum across Germany. Our analysis includes a statistic for the comparison of spatial and classical (non‐spatial) indices. We find that our spatial indices tend to result in higher values than classical ones. These differences are statistically significant at medium and high autocorrelation levels. We conclude that these spatial accuracy measures may contribute to evaluate prediction errors in presence/absence models, especially in case of medium or high degree of similarity of adjacent data, i.e. aggregated (clumped) or continuous species distributions.