Relating landscape to stream nitrate-N levels in a coastal eastern-Atlantic watershed (Portugal)

We apply a linear regression mixed effects model to explore the influence of landscape factors on nitrate-N concentrations in a coastal watershed of Portugal. Landscape composition and configuration metrics, together with variables assessing the physical characteristics of the study area, were used. The analysis was performed using seasonal data from the years 2001 and 2006. The seasonal influence was included as a random effect to account for temporal correlations. Together, the fixed and the random factors explain 78% of the variance, whereas the fixed factors alone explain 10%. Urban, slope, elevation and aggregation index of urban class contribute to the differences found in the NO₃⁻-N concentrations. Urban has the weakest effect, whereas slope and elevation show a conditioned negative effect on nitrate-N. The effect of slope gets stronger for higher standard deviations of elevation and the effect of the standard deviation of elevation, measuring the variation of elevation within a sub-watershed, gets stronger for steeper slopes. Of the configuration class level metrics included in the analysis, only aggregation index of urban played a significant role in the final model, and it revealed to be related to urban percentage. The influence of landscape configuration metrics, though observed by others, was not obvious in this study. Future analysis evaluating the effect of metrics selection could be performed.     

Evaluating empirical scaling relations of pattern metrics with simulated landscapes

Landscape patterns demonstrate scale-dependent properties that have been parsimoniously described by empirical scaling functions. These functions, derived from multiple-scale analysis of real landscapes, are evaluated here for their generality and robustness via a series of simulated landscapes with known landscape patterns. A factorial design was used to generate these landscapes, varying the number of classes, class abundance distribution, and patch dispersion. The results confirm that the three types of scaling relations were both general and robust. Type I metrics were predictable with simple scaling functions (e.g. power laws or linear functions); Type II metrics showed stair-case like response patterns and were essentially not predictable; Type III metrics exhibited erratic response patterns that were unpredictable in most cases. However, significant differences were found between real and simulated landscapes when landscape extent was increased. Systematic changes in grain size show that the predictability of scaling relations increases with the number of classes, the evenness of class abundance distribution, and the aggregation of patch dispersion. However, random patch dispersion seemed to enhance the predictability of scaling relations when changing spatial extent.     

A fish index of biotic integrity for South Dakota's Northern Glaciated Plains Ecoregion

Ecosystem goods and services in streams are impaired when their biotic communities are degraded by anthropogenic stressors. An index of biotic integrity (IBI) translates community structure into a standardized ecoregion-specific stream health score. Documenting stream health is especially important in the Northern Glaciated Plains (NGP) Ecoregion, which is undergoing rapid landscape alterations through increased agriculture production. Our objectives were to develop a fish IBI and validate candidate reference sites for NGP wadeable perennial streams. Fish were sampled from 54 sites (consisting of reference sites, known-condition least and most disturbed sites, and random sites) during summers 2006–2011. Candidate metrics were sorted into nine metric classes based on attributes of fish assemblage form and function. Metric values were screened using metric range, signal-to-noise ratios, responsiveness to disturbance, and redundancy tests until each metric class contained only those metrics most responsive to anthropogenic stressors. The final IBI consisted of six metrics that were reflective of prairie stream fish assemblages, and differentiated between known-condition least and most disturbed sites. The mean reference sampling site IBI scores were found to be similar to both least and most disturbed sites (Mann–Whitney U-test; P < 0.05). Twelve reference site scores were below the NGP's median (69), whereas the other 11 sites were above the median and were representative of least disturbed conditions. We now have developed a standardized bioassessment tool for evaluating stream health, as well as a baseline for long-term monitoring in a dynamic ecoregion.     

2D与3D景观指数测定山区植被景观格局变化对比分析

植被和土地覆盖变化是环境变化的一个重要因素,同时也是引起景观和生态系统变化的重要原因。景观指数是定量分析植被和土地覆盖变化的重要研究方法之一。以滇西北高山峡谷区为案例区,比较分析传统2D景观指数和3D景观指数进行植被变化定量测定的差异。研究主要选取了基于斑块面积和周长几个常用指数来进行比较分析。研究结果表明在斑块层次上,除了分维数指数,其他指数的三维方法计算值显著地高于二维方法计算值;在类型层次上,三维的类型面积指数、平均斑块面积指数、平均最小邻近距离指数测定的变化值显著大于二维的相应指数测定变化值,但是二维和三维平均形状指数和分维指数测定的植被斑块的平均形状变化结果没有显著差异;在景观层次,只有三维的平均斑块面积和最小邻近距离指数测定的变化结果显著高于二维的平均面积和最小邻近距离指数测定的变化结果,其它指数如形状指数、分维指数、多样性指数和均一度指数等测定出两个不同时期的植被图格局变化结果均无显著差异,主要由于这些指数是采用面积和周长的对数或者比值计算得出,从而缩小了斑块表面面积与平面面积,表面周长与平面周长之间的差异。总体而言,利用二维景观指数在进行定量分析山区植被格局变化时,往往低估了其类型面积、平均斑块面积、斑块邻近距离等指数变化量,而三维景观指数得到相对较精确的变化值。     

Evaluating the effectiveness of landscape configuration metrics from landscape composition metrics

Although landscape configuration and landscape composition metrics are correlated theoretically and empirically, the effectiveness of configuration metrics from composition metrics has not been explicitly investigated. This study explored to what extent substantial information of configuration metrics increases from certain easily calculated and extensively used composition metrics and how strongly the effectiveness is influenced by different factors. The effectiveness of 12 landscape configuration metrics from the percentage of landscape (PLAND) of each land-use class and patch density (PD) was evaluated through the coefficient of determination (R ²) of multivariate stepwise linear regression analysis of 150 town-based landscape samples from three regions. The different landscape configuration metrics from PLAND and PD presented significantly different performances in terms of effectiveness [the contagion index and aggregation index possess minimal information, and the effective mesh size (MESH) and area-weighted mean patch fractal dimension possess abundant information]. Furthermore, the effectiveness of configuration metrics showed different responses to changing cell sizes and different land-use categorization in different regions (interspersion and juxtaposition index, patch cohesion index, and MESH exhibited large variations in R ² among the different regions). No single, uniform, consistent characteristic of effectiveness was determined across different factors. This new approach to understanding the effectiveness of configuration metrics helps clarify landscape metrics and is fundamental to landscape metric assessment.     

Measures of co-expression for improved function prediction of long non-coding RNAs

Background

Almost 16,000 human long non-coding RNA (lncRNA) genes have been identified in the GENCODE project. However, the function of most of them remains to be discovered. The function of lncRNAs and other novel genes can be predicted by identifying significantly enriched annotation terms in already annotated genes that are co-expressed with the lncRNAs. However, such approaches are sensitive to the methods that are used to estimate the level of co-expression.

Results

We have tested and compared two well-known statistical metrics (Pearson and Spearman) and two geometrical metrics (Sobolev and Fisher) for identification of the co-expressed genes, using experimental expression data across 19 normal human tissues. We have also used a benchmarking approach based on semantic similarity to evaluate how well these methods are able to predict annotation terms, using a well-annotated set of protein-coding genes.

Conclusion

This work shows that geometrical metrics, in particular in combination with the statistical metrics, will predict annotation terms more efficiently than traditional approaches. Tests on selected lncRNAs confirm that it is possible to predict the function of these genes given a reliable set of expression data. The software used for this investigation is freely available.

     

Fractal dimension as an indicator for quantifying the effects of changing spatial scales on landscape metrics

While geographers and ecologists are well aware of the scale effects of landscape patterns, there is still a need for quantifying these effects. This paper applies the fractal method to measure the scale (grain or cell size) sensitivity of landscape metrics at both landscape and class levels using the Gold Coast City in Southeast Queensland, Australia as a case study. By transforming the original land use polygon data into raster data at eleven aggregate scales, the fractal dimensions of 57 landscape metrics as defined in FRAGSTATS were assessed. A series of linear log–log regression models were constructed based on the power law to obtain the coefficient of determination (COD or R²) of the models and the fractal dimension (FD) of the landscape metrics. The results show that most landscape metrics in the area and edge, shape and the aggregation groups exhibit a fractal law that is consistent over a range of scales. The six variations of several landscape metrics that belong to both the area/edge and shape groups show different scale behaviours and effects. However, the metrics that belong to the diversity group are scale-independent and do not accord to fractal laws. In addition, the scale effects at the class level are more complex than those at the landscape level. The quantitative assessment of the scale effect using the fractal method provides a basis for investigating landscape patterns when upscaling or downscaling as well as creating any scale-free metric to understand landscape patterns.     

Metrics on multilabeled trees: interrelationships and diameter bounds

Multilabeled trees or MUL-trees, for short, are trees whose leaves are labeled by elements of some nonempty finite set X such that more than one leaf may be labeled by the same element of X. This class of trees includes phylogenetic trees and tree shapes. MUL-trees arise naturally in, for example, biogeography and gene evolution studies and also in the area of phylogenetic network reconstruction. In this paper, we introduce novel metrics which may be used to compare MUL-trees, most of which generalize well-known metrics on phylogenetic trees and tree shapes. These metrics can be used, for example, to better understand the space of MUL-trees or to help visualize collections of MUL-trees. In addition, we describe some relationships between the MUL-tree metrics that we present and also give some novel diameter bounds for these metrics. We conclude by briefly discussing some open problems as well as pointing out how MUL-tree metrics may be used to define metrics on the space of phylogenetic networks.     

景观指数耦合景观格局与土壤侵蚀的有效性

景观格局分析是景观生态学中揭示景观变化及其生态效应的主要方法,而景观指数是景观格局分析中广泛使用的工具。土壤侵蚀是土壤物质在景观中的迁移和再分配过程,受地形、植被和人类活动及其空间格局的调控。运用景观格局分析揭示景观格局变化特别是土地利用/覆被格局变化对土壤侵蚀的影响是土壤侵蚀研究中应用景观生态学原理和方法的典型。在当前的研究中,斑块-廊道-基质范式下建立的景观指数对侵蚀过程的解释能力不断受到质疑,建立筛选适用的景观指数的原则和方法十分必要。以延河流域碾庄沟小流域为例,利用WATEM/SEDEM模型模拟多个年份流域侵蚀产沙和输沙量;基于土地利用/覆被数据,利用Fragstat4.2软件,计算了相应年份流域斑块、边界密度、形状、集聚与分散和斑块类型多样性4个方面的代表性景观指数。在此基础上,分析了景观指数与流域侵蚀产沙和输沙之间的关系,讨论了景观指数在土壤侵蚀研究中的有效性,在景观和斑块类型水平上分析了景观指数表达"源"、"汇"两大类景观类型的空间格局与侵蚀产沙和输沙之间的关系的一致性。结果表明:斑块-廊道-基底范式下发展的景观指数在指示景观格局的土壤侵蚀效应时存在局限。相对而言,斑块类型尺度的景观指数更能有效表达景观格局与土壤侵蚀的关系。基于景观类型在土壤侵蚀过程中的"源"、"汇"功能,提出了在土壤侵蚀研究中筛选适用的景观指数的原则:(1)对"源"、"汇"两类景观类型,景观指数与土壤侵蚀状况表征变量的相关系数符号相反;(2)对同为"源"或"汇"景观类型的多个景观类型,景观指数与土壤侵蚀表征变量的相关系数应具有符号一致性。尽管景观指数在斑块类型水平上具有一定的有效性,但用其预测景观格局变化的侵蚀效应有很大的不确定性。因此,基于土壤侵蚀过程与景观格局的作用机制发展新型的景观指数是增强景观格局分析预测土壤侵蚀过程的能力的途径。     

A consistent metric for nestedness analysis in ecological systems: reconciling concept and measurement

Nestedness has been widely reported for both metacommunities and networks of interacting species. Even though the concept of this ecological pattern has been well-defined, there are several metrics by which it can be quantified. We noted that current metrics do not correctly quantify two major properties of nestedness: (1) whether marginal totals (i.e. fills) differ among columns and/or among rows, and (2) whether the presences (1's) in less-filled columns and rows coincide, respectively, with those found in the more-filled columns and rows. We propose a new metric directly based on these properties and compare its behavior with that of the most used metrics, using a set of model matrices ranging from highly-nested to alternative structures in which no nestedness should be detected. We also used an empirical dataset to explore possible biases generated by the metrics as well as to evaluate correlations between metrics. We found that nestedness has been quantified by metrics that inappropriately detect this pattern, even for matrices in which there is no nestedness. In addition, the most used metrics are prone to type I statistical errors while our new metric has better statistical properties and consistently rejects a nested pattern for different types of random matrices. The analysis of the empirical data showed that two nestedness metrics, matrix temperature and the discrepancy measure, tend to overestimate the degrees of nestedness in metacommunities. We emphasize and discuss some implications of these biases for the theoretical understanding of the processes shaping species interaction networks and metacommunity structure.     

Blood pressure waveform contour analysis for assessing peripheral resistance changes in sepsis

Background

This paper proposes a methodology for helping bridge the gap between the complex waveform information frequently available in an intensive care unit and the simple, lumped values favoured for rapid clinical diagnosis and management. This methodology employs a simple waveform contour analysis approach to compare aortic, femoral and central venous pressure waveforms on a beat-by-beat basis and extract lumped metrics pertaining to the pressure drop and pressure-pulse amplitude attenuation as blood passes through the various sections of systemic circulation.

Results

Validation encompasses a comparison between novel metrics and well-known, analogous clinical metrics such as mean arterial and venous pressures, across an animal model of induced sepsis. The novel metric O_fe?→?vc, the direct pressure offset between the femoral artery and vena cava, and the clinical metric, ΔMP, the difference between mean arterial and venous pressure, performed well. However, O_fe?→?vc reduced the optimal average time to sepsis detection after endotoxin infusion from 46.2 min for ΔMP to 11.6 min, for a slight increase in false positive rate from 1.8 to 6.2%. Thus, the novel O_fe?→?vc provided the best combination of specificity and sensitivity, assuming an equal weighting to both, of the metrics assessed.

Conclusions

Overall, the potential of these novel metrics in the detection of diagnostic shifts in physiological behaviour, here driven by sepsis, is demonstrated.

     

To aggregate or not? Capturing the spatio-temporal complexity of the thermal regime

Freshwater stream systems are under immense pressure from various anthropogenic impacts, including climate change. Stream systems are increasingly being altered by changes to the magnitude, timing, frequency, and duration of their thermal regimes, which will have profound impacts on the life-history dynamics of resident biota within their home range. Although temperature regimes have a significant influence on the biology of instream fauna, large spatio-temporal temperature datasets are often reduced to a single metric at discrete locations and used to describe the thermal regime of a system; potentially leading to a significant loss of information crucial to stream management. Models are often used to extrapolate these metrics to unsampled locations, but it is unclear whether predicting actual daily temperatures or an aggregated metric of the temperature regime best describes the complexity of the thermal regime. We fit spatial statistical stream-network models (SSNMs), random forest and non-spatial linear models to stream temperature data from the Upper Condamine River in QLD, Australia and used them to semi-continuously predict metrics describing the magnitude, duration, and frequency of the thermal regime through space and time. We compared both daily and aggregated temperature metrics and found that SSNMs always had more predictive ability than the random forest models, but both models outperformed the non-spatial linear model. For metrics describing thermal magnitude and duration, aggregated predictions were most accurate, while metrics describing the frequency of heating events were better represented by metrics based on daily predictions generated using a SSNM. A more comprehensive representation of the spatio-temporal thermal regime allows researchers to explore new spatio-temporally explicit questions about the thermal regime. It also provides the information needed to generate a suite of ecologically meaningful metrics capturing multiple aspects of the thermal regime, which will increase our scientific understanding of how organisms respond to thermal cues and provide much-needed information for more effective management actions.     

Without quality presence–absence data,discrimination metrics such as TSS can be misleading measures of model performance

The discriminating capacity (i.e. ability to correctly classify presences and absences) of species distribution models (SDMs) is commonly evaluated with metrics such as the area under the receiving operating characteristic curve (AUC), the Kappa statistic and the true skill statistic (TSS). AUC and Kappa have been repeatedly criticized, but TSS has fared relatively well since its introduction, mainly because it has been considered as independent of prevalence. In addition, discrimination metrics have been contested because they should be calculated on presence–absence data, but are often used on presence‐only or presence‐background data. Here, we investigate TSS and an alternative set of metrics—similarity indices, also known as F‐measures. We first show that even in ideal conditions (i.e. perfectly random presence–absence sampling), TSS can be misleading because of its dependence on prevalence, whereas similarity/F‐measures provide adequate estimations of model discrimination capacity. Second, we show that in real‐world situations where sample prevalence is different from true species prevalence (i.e. biased sampling or presence‐pseudoabsence), no discrimination capacity metric provides adequate estimation of model discrimination capacity, including metrics specifically designed for modelling with presence‐pseudoabsence data. Our conclusions are twofold. First, they unequivocally impel SDM users to understand the potential shortcomings of discrimination metrics when quality presence–absence data are lacking, and we recommend obtaining such data. Second, in the specific case of virtual species, which are increasingly used to develop and test SDM methodologies, we strongly recommend the use of similarity/F‐measures, which were not biased by prevalence, contrary to TSS.     

Turing learning: a metric-free approach to inferring behavior and its application to swarms

We propose Turing Learning, a novel system identification method for inferring the behavior of natural or artificial systems. Turing Learning simultaneously optimizes two populations of computer programs, one representing models of the behavior of the system under investigation, and the other representing classifiers. By observing the behavior of the system as well as the behaviors produced by the models, two sets of data samples are obtained. The classifiers are rewarded for discriminating between these two sets, that is, for correctly categorizing data samples as either genuine or counterfeit. Conversely, the models are rewarded for ‘tricking’ the classifiers into categorizing their data samples as genuine. Unlike other methods for system identification, Turing Learning does not require predefined metrics to quantify the difference between the system and its models. We present two case studies with swarms of simulated robots and prove that the underlying behaviors cannot be inferred by a metric-based system identification method. By contrast, Turing Learning infers the behaviors with high accuracy. It also produces a useful by-product—the classifiers—that can be used to detect abnormal behavior in the swarm. Moreover, we show that Turing Learning also successfully infers the behavior of physical robot swarms. The results show that collective behaviors can be directly inferred from motion trajectories of individuals in the swarm, which may have significant implications for the study of animal collectives. Furthermore, Turing Learning could prove useful whenever a behavior is not easily characterizable using metrics, making it suitable for a wide range of applications.     

Remotely sensed vegetation phenology for describing and predicting the biomes of South Africa

Questions: What are the patterns of remotely sensed vegetation phenology, including their inter‐annual variability, across South Africa? What are the phenological attributes that contribute most to distinguishing the different biomes? How well can the distribution of the recently redefined biomes be predicted based on remotely sensed, phenology and productivity metrics? Location: South Africa. Method: Ten‐day, 1 km, NDVI AVHRR were analysed for the period 1985 to 2000. Phenological metrics such as start, end and length of the growing season and estimates of productivity, based on small and large integral (SI, LI) of NDVI curve, were extracted and long‐term means calculated. A random forest regression tree was run using the metrics as the input variables and the biomes as the dependent variable. A map of the predicted biomes was reproduced and the differentiating importance of each metric assessed. Results: The phenology metrics (e.g. start of growing season) showed a clear relationship with the seasonality of rainfall, i.e. winter and summer growing seasons. The distribution of the productivity metrics, LI and SI were significantly correlated with mean annual precipitation. The regression tree initially split the biomes based on vegetation production and then by the seasonality of growth. A regression tree was used to produce a predicted biome map with a high level of accuracy (73%). Main conclusion: Regression tree analysis based on remotely sensed metrics performed as good as, or better than, previous climate‐based predictors of biome distribution. The results confirm that the remotely sensed metrics capture sufficient functional diversity to classify and map biome level vegetation patterns and function.     

Evaluating the relative contributions of hydroperiod and soil fertility on growth of south Florida mangroves

The requirements of the European Water Framework Directive (WFD), aimed at an integrative assessment methodology for evaluating the ecological status of water bodies are frequently being achieved through multimetric techniques, i.e. by combining several indices, which address different stressors or different components of the biocoenosis. This document suggests a normative methodology for the development and application of Multimetric Indices as a tool with which to evaluate the ecological status of running waters. The methodology has been derived from and tested on a European scale within the framework of the AQEM and STAR research projects, and projects on the implementation of the WFD in Austria and Germany. We suggest a procedure for the development of Multimetric Indices, which is composed of the following steps: (1) selection of the most suitable form of a Multimetric Index; (2) metric selection, broken down into metric calculation, exclusion of numerically unsuitable metrics, definition of a stressor gradient, correlation of stressor gradients and metrics, selection of candidate metrics, selection of core metrics, distribution of metrics within the metric types, definition of upper and lower anchors and scaling; (3) generation of a Multimetric Index (general or stressor-specific approach); (4) setting class boundaries; (5) interpretation of results. Each step is described by examples.     

Statolith morphometrics as a tool to distinguish among populations of three cubozoan species

Timely identification of endangered populations is vital to save them from extirpation. Here we tested whether six commonly used early-warning metrics are useful predictors of impending extirpation in laboratory rotifer (Brachionus calyciflorus) populations. To this end, we cultured nine rotifer clones in a constant laboratory environment, in which the rotifer populations were known to grow well, and in a deteriorating environment, in which the populations eventually perished. We monitored population densities in both environments until the populations in the deteriorating environment had gone extinct. We then used the population-density time series to compute the early-warning metrics and the temporal trends in these metrics. We found true positives (i.e. correct signals) in only two metrics, the standard deviation and the coefficient of variation, but the standard deviation also generated a false positive. Moreover, the signal produced by the coefficient of variation appeared when the populations in the deteriorating environment were about to cross the critical threshold and began to decline. As such, it cannot be regarded as an early-warning signal. Together, these findings support the growing evidence that density-based generic early-warning metrics—against their intended use—might not be universally suited to identify populations that are about to collapse.     

Integrated multi-level quality control for proteomic profiling studies using mass spectrometry

Background

Evolutionary conservation of RNA secondary structure is a typical feature of many functional non-coding RNAs. Since almost all of the available methods used for prediction and annotation of non-coding RNA genes rely on this evolutionary signature, accurate measures for structural conservation are essential.

Results

We systematically assessed the ability of various measures to detect conserved RNA structures in multiple sequence alignments. We tested three existing and eight novel strategies that are based on metrics of folding energies, metrics of single optimal structure predictions, and metrics of structure ensembles. We find that the folding energy based SCI score used in the RNAz program and a simple base-pair distance metric are by far the most accurate. The use of more complex metrics like for example tree editing does not improve performance. A variant of the SCI performed particularly well on highly conserved alignments and is thus a viable alternative when only little evolutionary information is available. Surprisingly, ensemble based methods that, in principle, could benefit from the additional information contained in sub-optimal structures, perform particularly poorly. As a general trend, we observed that methods that include a consensus structure prediction outperformed equivalent methods that only consider pairwise comparisons.

Conclusion

Structural conservation can be measured accurately with relatively simple and intuitive metrics. They have the potential to form the basis of future RNA gene finders, that face new challenges like finding lineage specific structures or detecting mis-aligned sequences.     

On the Correlation between Reservoir Metrics and Performance for Time Series Classification under the Influence of Synaptic Plasticity

Reservoir computing provides a simpler paradigm of training recurrent networks by initialising and adapting the recurrent connections separately to a supervised linear readout. This creates a problem, though. As the recurrent weights and topology are now separated from adapting to the task, there is a burden on the reservoir designer to construct an effective network that happens to produce state vectors that can be mapped linearly into the desired outputs. Guidance in forming a reservoir can be through the use of some established metrics which link a number of theoretical properties of the reservoir computing paradigm to quantitative measures that can be used to evaluate the effectiveness of a given design. We provide a comprehensive empirical study of four metrics; class separation, kernel quality, Lyapunov''s exponent and spectral radius. These metrics are each compared over a number of repeated runs, for different reservoir computing set-ups that include three types of network topology and three mechanisms of weight adaptation through synaptic plasticity. Each combination of these methods is tested on two time-series classification problems. We find that the two metrics that correlate most strongly with the classification performance are Lyapunov''s exponent and kernel quality. It is also evident in the comparisons that these two metrics both measure a similar property of the reservoir dynamics. We also find that class separation and spectral radius are both less reliable and less effective in predicting performance.     

Motif-Role-Fingerprints: The Building-Blocks of Motifs,Clustering-Coefficients and Transitivities in Directed Networks

Complex networks are frequently characterized by metrics for which particular subgraphs are counted. One statistic from this category, which we refer to as motif-role fingerprints, differs from global subgraph counts in that the number of subgraphs in which each node participates is counted. As with global subgraph counts, it can be important to distinguish between motif-role fingerprints that are ‘structural’ (induced subgraphs) and ‘functional’ (partial subgraphs). Here we show mathematically that a vector of all functional motif-role fingerprints can readily be obtained from an arbitrary directed adjacency matrix, and then converted to structural motif-role fingerprints by multiplying that vector by a specific invertible conversion matrix. This result demonstrates that a unique structural motif-role fingerprint exists for any given functional motif-role fingerprint. We demonstrate a similar result for the cases of functional and structural motif-fingerprints without node roles, and global subgraph counts that form the basis of standard motif analysis. We also explicitly highlight that motif-role fingerprints are elemental to several popular metrics for quantifying the subgraph structure of directed complex networks, including motif distributions, directed clustering coefficient, and transitivity. The relationships between each of these metrics and motif-role fingerprints also suggest new subtypes of directed clustering coefficients and transitivities. Our results have potential utility in analyzing directed synaptic networks constructed from neuronal connectome data, such as in terms of centrality. Other potential applications include anomaly detection in networks, identification of similar networks and identification of similar nodes within networks. Matlab code for calculating all stated metrics following calculation of functional motif-role fingerprints is provided as S1 Matlab File.