What metric(s) is a good proxy for relatedness?

What metric(s) is a good proxy for relatedness?

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In attempting to seed a simulation, where an individual foregoes resources that go r to same-type individuals and (1-r) to all members (including same-type individuals). What commonly used metrics are a good proxy for r in this sense? In other words, if I am seeing a simulation to replicate the parameters of the mean population for, for example, a zebra finch or a human or a house mouse, and I am looking in empirical reports of these species and r does not appear, are there other metrics that are convertible?

Effective Coverage: A Metric for Monitoring Universal Health Coverage

A major challenge in monitoring universal health coverage (UHC) is identifying an indicator that can adequately capture the multiple components underlying the UHC initiative. Effective coverage, which unites individual and intervention characteristics into a single metric, offers a direct and flexible means to measure health system performance at different levels. We view effective coverage as a relevant and actionable metric for tracking progress towards achieving UHC. In this paper, we review the concept of effective coverage and delineate the three components of the metric — need, use, and quality — using several examples. Further, we explain how the metric can be used for monitoring interventions at both local and global levels. We also discuss the ways that current health information systems can support generating estimates of effective coverage. We conclude by recognizing some of the challenges associated with producing estimates of effective coverage. Despite these challenges, effective coverage is a powerful metric that can provide a more nuanced understanding of whether, and how well, a health system is delivering services to its populations.

Citation: Ng M, Fullman N, Dieleman JL, Flaxman AD, Murray CJL, Lim SS (2014) Effective Coverage: A Metric for Monitoring Universal Health Coverage. PLoS Med 11(9): e1001730.

Published: September 22, 2014

Copyright: © 2014 Ng et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: No specific funding was received for this study.

Competing interests: SL is a member of the Editorial Board of PLOS Medicine.

Abbreviations: ANC, antenatal care ART, antiretroviral therapy COPD, chronic obstructive pulmonary disease DHS, Demographic and Health Survey DOTS, directly observed treatment, short-course DSM, Diagnostic and Statistical Manual for Mental Disorders GA-BW, gestational age and birth weight–adjusted GBD 2010, Global Burden of Disease 2010 study HAART, highly active antiretroviral therapy IPTp, intermittent preventive therapy during pregnancy ITN, insecticide-treated net IV, instrumental variable MMR, measles-mumps-rubella SD, Symptomatic Diagnosis STDs, sexually transmitted diseases UHC, universal health coverage VA, verbal autopsy WHO, World Health Organization

Provenance: Not commissioned part of a Collection externally peer reviewed

This paper is part of the PLOS Universal Health Coverage Collection.

Summary Points

  • Effective coverage unites intervention need, use, and quality into a simple but data-rich metric, reflecting the core components of UHC.
  • Effective coverage can be applied to understand the health gains delivered by interventions at a range of levels, from individual benefits to national impact.
  • Effective coverage can be measured and used across resource settings. Lower-income countries can harness data from existing survey data to feed into effective coverage estimations.
  • The broader use of effective coverage remains hindered by the availability and quality of health data, especially at subnational levels.


As environmental problems worsen, researchers are directing their attention toward human-nature relationships and their effects on environmentally sustainable behavior. The construct of nature relatedness (NR and the self-report scale by the same name) captures individual differences in the way people view their relationship with the natural world (Nisbet et al., 2009). High nature relatedness, or a strong subjective connection with nature, is typically associated with greater happiness and environmental concern. Disconnection likely has harmful consequences for both human and environmental health, yet is a regular consequence of the modern lifestyles that often separate people (physically and psychologically) from the natural world. Thus, research on nature relatedness has potentially important implications, and the NR scale is increasingly used in research on sustainability and well-being. As these research contexts expand, the scale's length at 21 items can sometimes pose a problem. Here, we describe the development and validation of a new short version of the nature relatedness scale.

The theoretical background of nature relatedness draws on Wilson's (1984) biophilia hypothesis. He argued that because humans evolved in nature, we developed an innate need to connect with all life other living things supported our health and survival. The biophilia hypothesis helps to explain our connection (and the consequences of disconnection) with the natural world. Kellert and Wilson (1993) suggest that the learning and appreciation of biodiversity has likely been embedded in our biology, and that nature is essential for our health and development. The biophilia hypothesis has inspired a wide variety of research, for example, on landscape preference and phobias (Kaplan and Kaplan, 1989 Heerwagen and Orians, 1993 Ulrich, 1993). The popularity of outdoor wilderness activities, gardening, our relationship with animals, and our fondness for natural scenery are also evidence of biophilia (Lawrence, 1993 see Kahn, 1999, for a review). The mood (MacKerron and Mourato, in press), cognitive (Berman et al., 2008), health (Frumkin, 2001), and longevity (Mitchell and Popham, 2008) benefits associated with proximity to greenspace are also indicative of nature's importance for optimal health and well-being (see also the broad review by Selhub and Logan, 2012).

Despite these benefits, and people's evident attraction to nature, there are individual differences in how people connect with nature. Indeed, people relate to the physical environment differently, and these environmental dispositions are relatively stable and trait-like McKechnie (1977). Moreover, objective contact with nature is not fully equivalent to the subjective sense of connection that likely fosters sustainable attitudes and well-being. Some people may be very connected to their local ecosystems, while others may view themselves as completely separate from the natural environment. Urban-dwelling people, in particular, may have little or no contact with nature (Maller et al., 2005). People likely find it difficult to value and care for the environment if they feel separated from nature and it is not part of their experience. Individual differences in how connected people are with nature may reflect how aware they are of biophilia or how much their biophilic tendencies are supported or suppressed. Many people may have lost their connection to the natural world (Conn, 1998), and these damaged human-nature relationships may be contributing to environmentally destructive behavior as well as unhappiness.

Schultz (2000) argues that environmental concerns are directly related to the degree with which people see themselves as part of nature. In other words, if people do not value nature or care about the environment they are not likely to protect it (Howard, 1997). The more connected people are to nature, the more they will be aware of their own actions and be concerned for all living things (Schultz, 2000). This type of 𠇋iospheric” attitude reflects a strong human-nature connection, whereas exclusive concerns for one's self (𠇎goistic” concerns) indicate a damaged relationship. Nature relatedness and biospheric concern are associated with less self-interest and more consideration of the larger environment, in other words, more environmental concern (Schultz, 2000 Mayer and Frantz, 2004 Dutcher et al., 2007) and self-reported environmental behavior (Schultz, 2001, 2002 Clayton, 2003 Nisbet et al., 2009).

With continuing environmental destruction, loss of biodiversity, and species extinction, we may be losing many of the elements necessary to trigger and nurture our biophilia (Thomashow, 1998). A damaged environment is unlikely to extinguish our need to connect with nature, however, it may diminish our appreciation for the role of natural diversity in healthy physical and psychological development, and reduce the opportunity for future generations to benefit from nature (Kellert, 1997). The fact that our biophilic tendencies have not resulted in more widespread environmental behavior suggests value in further studying individual differences in levels of connectedness.

To that end, a number of assessment tools have been developed to capture subjective connectedness with nature (e.g., nature relatedness, connectedness to nature, connectivity with nature, environmental identity Schultz, 2000 Clayton, 2003 Mayer and Frantz, 2004 Dutcher et al., 2007 Nisbet et al., 2009). It is possible to make small distinctions among these constructs and assessment tools, but research to date suggests considerable similarity (Tam, 2013). Here we focus on the particular example of nature relatedness.

Nisbet et al. (2009) proposed the construct of nature relatedness to capture several facets of human-nature relationships𠅌ognition, affect, and experience𠅊nd to measure people's interest in, fascination with, and desire for nature contact. Nature relatedness is similar to the notion of an ecological identity (a sense of self that includes nature), but is a broader concept encompassing emotions, experiences, and an understanding of human interconnectedness with all other living things. Nature relatedness is not simply a love of nature, or enjoyment of only the superficially pleasing facets of nature, but rather an awareness and understanding of all aspects of the natural world, even those that are not aesthetically appealing or useful to humans. Nature relatedness may be indicative of how much an innate need to connect with nature (biophilia) has (or has not) been nurtured. Considering the biophilia hypothesis, it also follows that a strong sense of nature relatedness should predict happiness and well-being more broadly.

Psychologists conceptualize happiness, or subjective well-being, in a variety of ways. One approach to defining subjective well-being𠅊 hedonic approach—is to focus on the quantity of positive and negative emotions, and satisfaction with one's life (Diener, 2000). Another approach, from a humanistic perspective, uses the term psychological well-being and includes other adaptive characteristics such as sense of purpose and meaning in life (Ryff and Keyes, 1995). Drawing on both traditions, research on nature relatedness has considered happiness as a multidimensional construct and has assessed links with hedonic and eudaimonic indicators. For example, people higher in trait nature relatedness report more life satisfaction, vitality, and positive affect, as well as greater purpose in life, autonomy, and personal growth (Howell et al., 2011 Nisbet et al., 2011 Tam, 2013). The association between happiness and nature relatedness remains (or, in some cases, becomes stronger) after controlling for environmental attitude measures. Controlling for other subjective connections (e.g., with family, culture, group identities) also does not remove the link between nature relatedness and happiness (Zelenski and Nisbet, in press). Thus, it appears that nature relatedness is distinct from both environmental attitudes and a general sense of connection. There is something special about how people view their relationship with nature.

The construct of nature relatedness has been useful in understanding individual differences in environmental behavior and well-being. As interest in the construct has grown, it has also become clear that the scale's length of 21 items makes it too unwieldy for some research contexts. We therefore undertook an effort to develop a short version that was similar to the original (e.g., in terms of its content and correlates) and retained good psychometric properties. Two similar brief measures currently exist. Schultz's (2001) inclusion of nature in self (INS) is a single item, and Dutcher et al.'s (2007) connectivity with nature is five items (that includes the single INS item). (Other similar scales have 㸐 items). Although these measures likely provide reasonable substitutes, we note that a recent comparison found them to be somewhat less reliable, less strongly related, and somewhat distinct in predicting outcomes, compared to longer scales (Tam, 2013). Thus, there appears to be potential in a new abbreviated scoring of the nature relatedness scale.

To create the short version, we selected items from the 21-item scale that were representative of the theoretical foundations of the nature relatedness construct. Drawing on data from over 1200 previous participants, we examined frequency distributions to find items that discriminate low from highly nature related people well, and looked for items that had relatively normal distributions. We also examined individual items' correlations with other conceptually related scales that assessed environmental attitudes and subjective well-being. Using these criteria, we selected six nature relatedness items that performed very similarly to the full 21-item scale. Four of the items assess self-identification with nature, a sense of connectedness that may be reflected in spirituality, awareness or subjective knowledge about the environment, and feelings of oneness with nature: “I always think about how my actions affect the environment,” “My connection to nature and the environment is a part of my spirituality,” “My relationship to nature is an important part of who I am,” and “I feel very connected to all living things and the earth.” Two additional items capture individual differences in the need for nature and comfort with wilderness, as well as awareness of local wildlife or nearby nature: “My ideal vacation spot would be a remote, wilderness area” and “I take notice of wildlife wherever I am” (Appendix A).

It is worth noting that the six selected items represent only two of three factors observed in an exploratory factor analysis of the full scale (Nisbet et al., 2009). Those subscales were interpreted as a sense of identification (“self”), contact with nature (𠇎xperience”), and pro-nature conservation attitudes (“perspective”). Although initially hesitant to omit all perspective items, we ultimately concluded that this was the best choice for a few reasons. Perhaps obviously, our selection was done relatively blind to the subscales, i.e., we followed reasonable criteria without regard to them. The data strongly informed which items were selected, and it is indeed telling that validity correlations, particularly those with environmental attitudes, were strong without the pro-conservation items. At a conceptual level, identification and actual connection with nature seem more central to the construct of nature relatedness, and it seems fitting that these subscales form the essential items of a short version. It may be that the “perspective” subscale assesses something that is less nature relatedness and more the related, but distinct, construct of pro-environmental attitudes.

We present data from four studies that assessed the links among nature relatedness, environmental attitudes, and subjective well-being. The first three of these draw on archival findings, comparing the new short scoring to the full scale in the data we used to initially validate the full scale and more recently used to select the short scale items. We then present new data, i.e., collected after the new scoring was determined, that replicates and further validates the short version of the scale. Across studies, we expected the NR-6 measure to correlate positively with happiness and environmental behavior. We also anticipated that the short-form NR scale would predict the amount of time people spend in contact with nature.

1 Answer 1

DNS and ICMP are not good examples, because in a well configuration, network access is restricted by use of a proxy to thwart DNS and ICMP right from the start.

But there‘s HTTP(S) still available through the proxy.

Usually, websites are not whitelisted in a proxy, so this should work quite well.

As to the content inspection:

You can not do that in general. Even if you have a HTTPS proxy that is doing a MITM, the request payload might still be encrypted within the transport of HTTPS which cannot be scanned for key words.

Additionally, you may not be allowed to MITM for example banking sites for inspection due to normative regulations.

You can however try and find encrypted payload streams in HTTP(S) traffic and analyze them, in the mean time blacklisting the destination hosts.

While volume based exfiltration detection seems like a good plan for DNS and ICMP, for HTTPS it is not as there might be legitimate traffic exceeding your threshold resulting in a big number of false positives and possibly business disruption.

A combined approach would maybe work, maybe with a white list of domains that are safe to upload data to: if you find a significant amount of data is sent through the proxy, you could blacklist the destination if it’s not on the white list and analyze the traffic manually. If the exfiltrated data is small in size, you might still not get it.

All in all: that’s the problem with covert exfiltration: it’s hidden well.

Phylogenetic relatedness as a tool in restoration ecology: a meta-analysis

Biotic interactions assembling plant communities can be positive (facilitation) or negative (competition) and operate simultaneously. Facilitative interactions and posterior competition are among the mechanisms triggering succession, thus representing a good scenario for ecological restoration. As distantly related species tend to have different phenotypes, and therefore different ecological requirements, they can coexist, maximizing facilitation and minimizing competition. We suggest including phylogenetic relatedness together with phenotypic information as a predictor for the net effects of the balance between facilitation and competition in nurse-based restoration experiments. We quantify, by means of a Bayesian meta-analysis of nurse-based restoration experiments performed worldwide, the importance of phylogenetic relatedness and life-form disparity in the survival, growth and density of facilitated plants. We find that the more similar the life forms of neighbouring plants are the greater the positive effect of phylogenetic distance is on survival and density. This result suggests that other characteristics beyond life form are also contained in the phylogeny, and the larger the phylogenetic distance, the less is the niche overlap, and therefore the less is the competition. As a general rule, we can maximize the success of the nurse-based practices by increasing life-form disparity and phylogenetic distances between the neighbour and the facilitated plant.

1. Introduction

The unprecedented level of native habitat perturbation and the concomitant loss of biodiversity demand that ecologists fill the gap between restoration science and practice [1,2]. This means that ecological restoration will be a key process for the conservation of biodiversity, which can benefit from the fast-growing body of knowledge acquired among disciplines such as community ecology or evolutionary ecology. In this regard, one of the big challenges is to determine the way plant communities are assembled through biotic interactions [3], or, in other words, to know how redundant different species are in communities [4] for restoration purposes.

Biotic interactions assembling plant communities can be positive or negative and they usually operate simultaneously [5]. For example, a nurse plant may buffer extreme air temperatures, enhancing the establishment of other plants (i.e. facilitation), but may also limit the growth of the facilitated plants by reducing the availability of nutrients (i.e. competition). Understanding the net effects of the combination of facilitation and competition is therefore crucial to determine the performance of the species involved in the interaction. Furthermore, facilitative interactions, together with posterior competition, are among the proposed mechanisms triggering succession [6] and such succession may lead disturbed communities towards steady states very similar to the undisturbed community (see [7,8] for experimental evidence). Despite the good opportunity that plant facilitation represents for the ecological restoration of disturbed communities, it has not been used for restoration purposes until the 21st century [9]. Such a gap is consistent with the traditional lack of attention that positive interactions have suffered under the predominant view of competition as an omnipresent force shaping ecological interactions and communities [10]. In contrast to competition-focused afforestation techniques, in which seedlings are planted after eliminating the pre-existing vegetation, restoration based on facilitation, also known as nurse-based restoration, consists of planting the plants spatially associated with other plants, which provides them with a favourable microhabitat [11]. Nurse-based restoration experiments have been increasingly performed in different types of ecosystems worldwide, with varying success (see [12] for a recent review). Similarly to other restoration approaches, such as the framework species method [13], nurse-assisted planting may promote more rapid natural succession in disturbed habitats [14]. For example, nurse-based restoration accelerates the recovery of the structure of a burnt Mediterranean community by increasing species diversity and evenness [7].

Species behaving as good nurses able to launch succession in semi-arid communities have morphological and functional characteristics different from the beneficiary plant species [14–16]. Interestingly, such characteristics are also valuable predictors of how good a species is as a nurse in restoration experiments [12]. This result is consistent with the limiting similarity concept, which predicts that species with similar traits will not coexist in the community because of great niche overlap [17] (but see [18] for alternative coexistence mechanisms). This concept has been suggested as a framework to develop trait-based community assembly restoration practices of invaded systems [19]. Similarly, we suggest that nurse-based restoration practices may benefit from the inclusion of phenotypic information by ensuring that the nurse and the facilitated plant species are phenotypically different. But phenotype is composed by a complex array of interacting traits that are not always measurable. Under this situation, phylogeny can inform us about the unmeasured dimensions of the phenotype given that most of the traits are evolutionarily conserved [20]. If all the relevant information were known about species traits then phylogeny would not provide additional information [21]. However, we seldom have all the relevant phenotypic information, or, even worse, we ignore what is the relevant trait for the success of the facilitated plant. When traits are evolutionarily conserved, the phenotype of one species is expected to resemble that of closely related species. Thus, by looking at the phylogenetic distance between two species, we can infer the phenotypic distance between them. Obviously, this inference will not work under evolutionary trait convergence or fast phenotypic divergence [22–25]. Under trait convergence, distantly related species will be phenotypically similar, but under fast trait divergence, phylogenetic distance may be a wrong predictor of the phenotypic distance with respect to interaction outcomes. In the latter case, phenotypic differences are so large that phylogenetic differences are irrelevant. The existence of all these different possibilities emphasizes the necessity to test the assumption of using phylogenetic information as a proxy for phenotypic and niche dimensions [24].

The facts that facilitative interactions in nature occur between distantly related species and competition occurs between closely related species support the rationale of using phylogenetic relatedness as a proxy for the net effects of the balance between facilitation and competition [26–29] (but see [30]). In addition, such phylogenetic signature, together with experimental evidence, indicates that facilitative and competitive interactions are, to some extent, species-specific, and hence it is relevant to select the correct pairs of nurse and facilitated plant species for restoration practices [31,32].

Our goal in this study is to unite the principles of ecology and evolutionary biology to show that a phylogenetic framework can be used successfully to significantly improve efforts to restore disturbed habitats. As far as we know, no study on ecological restoration has integrated phylogenetic information to better predict the success of the planned activities. We suggest that coexistence between distantly related species produced by phenotypic disparity is a general solution to ecological restoration problems everywhere. For phenotypic and phylogenetic variables to be widely useful in ecological restoration, especially in areas where species databases are not available, they should be extremely easy to collect. For easiness of measure, we selected the life-form disparity and the phylogenetic distance between the neighbour and the facilitated species. Life form may encapsulate a complex array of phenotypic characters and has been proved to be determinant in the outcome of the nurse-based restoration experiments [12,14]. Phylogenetic distances can be easily obtained with the help of a Web and iPhone application named T ime T ree , which is a public knowledge-base of divergence times [33]. By using these two simple measures in a Bayesian meta-analysis of nurse-based restoration experiments performed across different ecosystems worldwide, we quantify the importance of phylogenetic relatedness and life-form disparity to predict the success of facilitated plants in terms of survival, growth and density. Given the species-specificity of facilitation and competition, we also quantify the relative importance of the identity of the nurse and the facilitated species to the outcome of the interaction.

2. Material and methods

(a) Database

We used the database compiled by Gómez-Aparicio [12], consisting of published studies where interactions among plants were manipulated to restore degraded habitats worldwide from temperate and tropical humid and semi-arid ecosystems, as well as wetlands. The effect of neighbours on the facilitated (hereafter target) plant performance components such as emergence, survival, growth and density was estimated as a function of several predictors such as study duration, the life form of the neighbour and target species, and the ecosystem type. As we are interested in pairwise interactions between a neighbour and a target species, we excluded from the database those cases where several neighbour or several target species were mixed in the same experiment. The final database for survival analyses yielded a total of 31 studies containing 188 suitable cases with 52 neighbour and 75 target species. For growth (measured as biomass or height) analyses, we used 22 studies containing 85 suitable cases with 38 neighbour and 50 target species. For density (measured as the number of individuals or cover per a given area) analyses, 17 studies were used, finally yielding 56 suitable cases with 20 neighbour and 34 target species. Emergence could not be analysed because of the low sample size. The final database is available in the electronic supplementary material.

For each selected study we took (i) the identity of the neighbour species, (ii) the identity of the target species, and (iii) the effect size and its variance. Effect size indicates the magnitude of the neighbour effect on survival, growth or density of target plants in relation to the open ground. The effect size for survival (ln (OR)) was calculated as the natural log of the ratio of the odds of survival in the presence of neighbours (experimental group) to the odds of survival in their absence (control group). The effect sizes for growth and density data (ln(RR)) were calculated as the natural log of the ratio of the mean outcome in the experimental group to that of the control group. Effect sizes greater than zero indicate a positive effect of neighbours on target plants (facilitation), whereas values lower than zero indicate a negative effect of neighbours (competition). All effect sizes and the associated variances are shown in the electronic supplementary material.

We added to the database two new variables intended to capture phenotypic and phylogenetic disparity between the nurse and the facilitated plants. For simplicity, we selected the life-form disparity between the neighbour and the target species as a measure of phenotypic disparity. Life-form disparity was calculated as the absolute difference between the life forms of the neighbour and target species after coding life forms as 1 = herbs, 2 = shrubs and 3 = trees. Phylogenetic distance was calculated as the distance (in million years, Myr) connecting neighbour and target species in the phylogenetic tree through their most recent common ancestor. The phylogenetic distances were obtained from a phylogeny generated with the help of the program P hylomatic [34]. This program generates a phylogenetic tree by matching the family names of our study species with those contained in a backbone phylogeny, which is the megatree based on the work of the Angiosperm Phylogeny Group [35]. The nodes of the tree were dated with the help of the bladj algorithm implemented in P hylocom 3.41 software [34]. This algorithm dates the nodes based on the ages of Wikström et al.'s [36] database and distributes evenly the undated nodes between the dated nodes. To ensure that our phylogenetic distances were similar to those obtained with the T ime T ree application, we correlated the phylogenetic distances obtained with P hylomatic and T ime T ree applications and found a high degree of correlation (r = 0.90 n = 75, p < 1 × 10 −15 ).

(b) Statistical analyses

We ran Bayesian meta-analyses by fitting generalized linear mixed models using Markov chain Monte Carlo (MCMM) techniques with the help of the MCMCglmm package for R [37]. The effect size of survival, growth or density was the dependent variable in the model and their variances were passed to the mev argument of MCMCglmm [38]. Life-form disparity and the logarithm of phylogenetic distance between neighbour and target species were included as predictors. Different sources of pseudoreplication can be accounted for in this analysis (i.e. study, species, author, country, etc.) and we focused on that coming from the use of the same species in different experiments. Thus, the identities of neighbour and target species were included as random, grouping factors.

We ran 13 000 MCMC iterations, with a burn-in period of 3000 iterations and convergence of the chain tested by means of an autocorrelation statistic. The default priors (ν = 0, V = 1) were used except for growth analyses where a stronger prior (ν = 1, V = 0.002) was required owing to numerical problems of singularity in the mixed model equations. To assess the sensitivity of the analyses to alternative prior specifications, we re-ran all the models with different priors, and results were consistent.

The overall effect size was estimated by running the models without predictors. The effect of predictors (life-form disparity and phylogenetic distance) was estimated by calculating the 95% credible interval of their posterior distribution and computing the probability that such effect is larger than zero (pMCMC). The proportion of remaining variance explained by each grouping factor (neighbour and target species identity) was estimated by calculating the 95% credible interval of its posterior distribution. It should be noted that this interval will never contain zero because variances are bounded to be positive [39]. Therefore, a wide credible interval with an extremely low bound suggests an insignificant effect of the grouping factor.

To quantify at what phylogenetic depth our results were occurring, we re-ran the analyses after sequentially removing cases with different phylogenetic distance between the neighbour and target species (from 0 to 300 Myr, each 50 Myr).

3. Results

The study cases contained the whole range of life-form disparities and a wide range of phylogenetic distances between neighbours and their target plants (figure 1). Phylogenetic distance was significantly correlated with life-form disparity across the whole database (F1,174 = 40.2 p < 0.001) but only explained 18 per cent of the variance. This low percentage indicates that phylogeny may still contain additional information about the similitude of species traits others than life-form disparity. This fact allows us to test the role of both variables on the effects of neighbours on survival, growth and density of target plants.

Figure 1. Frequency distribution of life-form disparity and phylogenetic distance values between the neighbour and target plant species in the final database. Life-form disparity is the difference between the life forms of the neighbour and target species when coded as 1 = herbs, 2 = shrubs and 3 = trees. Phylogenetic distance is the distance (Myr) connecting neighbour and target species in the phylogenetic tree through their most recent common ancestor.

Across all the studies, neighbours had an overall positive effect on survival (effect size = 0.42 [0.22, 0.61] 95% credible interval pMCMC < 0.001). When including the species identities and predictors in the model (table 1), the positive effect on survival strongly increased with life-form disparity between the neighbour and the target plant. The significant negative interaction between life-form disparity and phylogenetic distance indicates that the lower the disparity between the life forms of the two species, the higher the effect of their phylogenetic distance on survival of the target species. The identity of the neighbour explained a percentage of remaining variance ranging from 32 to 57 per cent, whereas the identity of the target plant was irrelevant to explain the effects of neighbours on plant survival. All these results were robust to the removal of cases in which the phylogenetic distance between the neighbour and the target species was lower than 100 Myr.

Table 1. Effect of neighbours on survival of target plants as a function of the life-form disparity (LFdisp) and phylogenetic distance between the neighbour and the target species. Neighbour and target species identity were considered as random factors.

The neighbour's overall effects on the growth of target plants across all studies was not significant (effect size = 0.05 [−0.09, 0.61] 95% credible interval pMCMC = 0.44). Also, the model including predictors and species identities (table 2) indicated that neither life-form disparity nor phylogenetic distance was relevant to explain the effect of neighbours on the growth of target plants. The identity of the neighbour did not explain a significant portion of the remaining variance, but that of the target explained between 19 and 39 per cent. Results were consistently non-significant after removing cases at different phylogenetic distances.

Table 2. Effect of neighbours on growth of target plants as a function of the life-form disparity (LFdisp) and phylogenetic distance between the neighbour and the target species. Neighbour and target species identity were considered as random factors.

Density of target plants was negatively affected by the presence of neighbours (effect size = −0.36 [−0.69, −0.02] 95% credible interval pMCMC = 0.04). The model with predictors and species identities (table 3) shows that such a negative effect was alleviated with increasing life-form disparity and phylogenetic distance. The significant interaction term indicates that the beneficial effect of phylogenetic distance on density increases when life forms of the neighbour and target plant species are similar. The identity of both neighbours and target species did not explain a great proportion of remaining variance in the model, as suggested by the wide confidence interval having its lower limit close to zero. All these results were robust to the removal of cases in which the phylogenetic distance between the neighbour and the target species was lower than 260 Myr.

Table 3. Effect of neighbours on density of target plants as a function of the life-form disparity (LFdisp) and the phylogenetic distance between the neighbour and the target species. Neighbour and target species identity were considered as random factors.

4. Discussion

Traditional restoration practices were based on the elimination of assumed competitors by eliminating pre-existing neighbours, but now the increasing evidence that positive interactions between plants may facilitate species coexistence and trigger succession has recently led to nurse-based restoration practices [9,11]. Facilitation tends to occur between distantly related species, whereas competition tends to be high between closely related species [27]. Coexistence of distantly related species and the competition–relatedness hypothesis formulated by Darwin are two sides of the same coin. However, while competition has been repeatedly invoked in ecology, coexistence mediated by positive interactions has not [10]. Here, we show how phylogenetic relatedness among species can be used as an informative tool in nurse-based restoration practices.

The effects of neighbours on target plants in restoration experiments worldwide were positive for survival, neutral for growth and negative for density. The positive effects of neighbour plants on survival of target plants increase when both species have different life forms. In such a case, the phylogenetic distance between both species is not very relevant for survival. This is consistent with the finding that congeneric Opuntia species may coexist when morphological disparity is high (erect versus decumbent platyopuntias [40]). However, when both species have the same life form, phylogenetic distances should be maximized to ensure that other phenotypic traits do differ. This is because other characteristics beyond life form are also contained in the phylogenetic information, and the larger the phylogenetic distance, the less the niche overlap, and therefore the less the competition. The negative effects of neighbours on the density of target plants can be mitigated with increasing life-form disparity and phylogenetic distance. If neighbour and target species belong to the same life form, we should again ensure that both species are phylogenetically distant to minimize the negative effects of neighbours on the density of target plants. Our analyses revealed that the minimum phylogenetic distance between both species to ensure survival should be around 100 Myr, but much longer (260 Myr) to minimize negative effects on density. Interestingly, this age falls within the range of mean phylogenetic distance between nurses and beneficiary plants found in natural communities (244–343 Myr [28] and [29] for Mexican desert and Mediterranean shrub communities, respectively).

Our results clearly show that complementing phenotypic with phylogenetic information is useful to predict the success of nurse-based restoration practices. This approach has proved useful at the community and ecosystem levels. At the community level, morphological and phylogenetic distances between alien and native plants significantly explain the impact of invaders in the reproductive success of co-flowering native plants. On the one hand, the effect of aliens on visitation and reproductive success was most detrimental when alien and focal species had similar flower symmetry or colour, and on the other hand, the phylogenetic relatedness between alien neighbours and focals influenced the reproductive success effect size [41]. At the ecosystem level, phylogenetic and functional diversity complement each other as predictors of the effect of biodiversity on ecosystem functioning in grassland biodiversity–ecosystem functioning experiments [42]. We are confident that our results, although based on pairwise interactions, can be applied to restoration to communities with multiple species. In fact, Castillo et al. [43] have shown experimentally that phylogenetic relatedness can be successfully used as a predictor of plant performance in multi-specific assemblages.

It is well known that not all species of competitors have equivalent effects on a target species [44,45]. Species-specific differences in competitive effects have been found in many neighbourhood analyses [44,46–49]. Similarly, species-specificity in facilitative interactions also occurs, and the identity of both the nurse and the target plant is relevant to understand the outcome of the interaction [31,50,51]. Here, we have quantified for the first time the relative importance of the taxonomic identities of the neighbour and the target plant species in the outcome of the interaction established in restoration experiments. These results show that the identity of the neighbour is strongly relevant for the survival, but not for the growth or density, of the target species. On the other side, the identity of the target plant is only relevant to explain the neighbour's effect on its growth rate. All these results are consistent with the species-specificity shown by both nurses and facilitated plant species in facilitation and competition networks [51]. Such species-specificity follows a non-random phylogenetic pattern, indicating that phylogenetic history has a pervasive influence not only on recruitment stages where facilitation predominates, but also on adult stages where competition starts to act. Given the concordance of results found in nature with those obtained in restoration experiments, we recommend the inclusion of phylogenetic information in restoration practices.


We thank S. Beaudry, K. Kulacki, M. McCarthy, E. Mitchell, P. Parent, C. Wilson and C. Zhou for technical assistance during the experiment. This work was supported by the U.S. National Science Foundation's DIMENSIONS of Biodiversity programme in a grant to B.J.C. (DEB-1046121) and to T. H. O. (DEB-1046121).

Figure S1. Unsmoothed molecular phylogeny including 37 common North American freshwater green algae genera and 3 out-groups estimated using partial 18S ribosomal RNA and rbcl sequences available on GenBank.

Figure S2. Smoothed molecular phylogeny including 37 common North American freshwater green algae genera and 3 out-groups estimated using partial 18S ribosomal RNA and rbcl sequences available on GenBank.

Table S1. Contingency Tables of the chi-square tests on the prevalence of facilitation or competition.

Table S2. Raw data including the initial and final densities of invaders.

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The dimensions classification framework unites metrics developed across ecological sub-disciplines and used for different purposes. However, the framework does not easily resolve the problem of choosing among metrics for a particular analysis. Ecological questions, whether from conservation, community ecology, or macroecology, all consider how accumulated differences between species (reflected by divergences along a phylogeny) may relate to biological processes or patterns. Evolutionary history can be an outcome or predictor of processes of interest. We suggest that questions about these processes or patterns can be simplified and unified to recognize the three general themes of: how much total diversity is present in an assemblage (or among assemblages) how different, on average, are taxa in an assemblage (or among assemblages) and/or how regular or variable are the differences between taxa in an assemblage (or among assemblages) (Fig. 5). We review below the types of questions asked by ecologists using phylogenies, identify commonalities, and connect these questions with appropriate phylo-diversity metrics.

(1) Applying richness metrics

Richness metrics can be used to measure or describe observed patterns of diversity these values may also be compared to equivalent taxonomic and functional measures. As richness metrics sum the quantity of phylogenetic differences in an assemblage, they are often assumed to capture ‘feature diversity’ under some models of trait evolution (Kelly, Grenyer & Scotland, 2014 ). In this context, measures of phylogenetic richness may be used to answer questions about the quantity and distribution of extant biodiversity, arguably better than species-based metrics (Rosauer & Mooers, 2013 ). Phylogenetics offers metrics which are relatively insensitive to taxonomic inflation (Isaac, 2007 ), and which can easily incorporate taxa (or other evolutionary units) for which there is little information, other than their placement on the tree of life. Feature diversity may be considered a valuable indicator of either future utility or future evolutionary potential (Mace, Gittleman & Purvis, 2003 Forest et al., 2007 ) and so conservation biologists have been interested in the protection of total feature diversity for questions of prioritization of taxa and/or areas (e.g. Forest et al., 2007 Isaac, 2007 Purvis, 2008 Rodrigues et al., 2011 Bennett et al., 2014 Jetz et al., 2014 ). For example, Tucker et al. ( 2012 ) asked how Proteaceae phylogenetic diversity was distributed spatially in the Cape Floristic Province. To capture the total evolutionary richness in a spatial unit, they considered two richness metrics – PD, and the sum of abundance-weighted ED (BED) – and compared the distributions of these metrics with Proteaceae species richness in the region.

Phylogenetic richness (either α- or β-diversity) has also been used as a predictor or response variable in numerous studies, across multiple spatial or temporal scales and for diverse natural systems. Variation in phylogenetic richness through space and time is often hypothesized to be an outcome of different ecological and evolutionary processes (Cavender-Bares et al., 2009 Mouquet et al., 2012 ). For example, as invasive species represent a non-random combination of traits, and phylogenetic metrics can be used to capture such ‘feature diversity’, it may be hypothesized that invasion should lead to differential changes in phylogenetic richness (α- or β-diversity) compared to species richness. Winter et al. ( 2009 ) tested this by comparing taxonomic and phylogenetic richness metrics in invaded assemblages [ultimately showing that alien species led to a decrease in phylogenetic distinctness (i.e. divergence) rather than richness]. In a separate application Thuiller et al. ( 2011 ) found that species' vulnerability to climate change clustered weakly along the phylogeny, and used this relationship to predict how the amount and distribution of phylogenetic richness will change in the future.

(2) Applying divergence metrics

Questions about ecological communities have frequently considered phylogenetic distance to be a proxy for differences in functional traits (Ackerly, 2009 reviewed in Freckleton, Harvey & Pagel, 2002 Mouquet et al., 2012 Srivastava et al., 2012 ), with the assumption that closely related species are more functionally similar, and thus overlap more in their ecological niche, than those that are more distantly related (Connolly et al., 2011 Gerhold et al., 2015 but see Narwani et al., 2013 Purschke et al., 2013 Violle et al., 2011 ). Underlying this are additional assumptions that closely related species occur in sympatry and that trait evolution is divergent, so the most similar taxa are the most closely related (Gerhold et al., 2015 ). When these assumptions hold, it is often hypothesized that if environmental filtering drives community assembly, taxa within an assemblage will be more related on average than expected in a random or null assemblage (Cavender-Bares & Wilczek, 2003 but see Mayfield & Levine, 2010 Webb et al., 2002 ). Alternatively, if competitive interactions are important, it may be hypothesized that co-occurring taxa will be less related (i.e. more divergent) than expected on average. Divergence indices, particularly MPD and MNTD indices, have been used to test these types of hypotheses about the mean relatedness of taxa within an assemblage. For example, Helmus et al. ( 2010 ) considered whether disturbed communities tended to contain more closely related species, reflecting the role of environmental filtering in selecting disturbance-tolerant taxa. They hypothesized that more closely related species might have similar traits, and so be similarly adapted to disturbance conditions. To test this, the authors used the PSV metric, which is closely related to MPD, and compared the average relatedness of species in disturbed communities versus non-disturbed communities.

Note that although these are frequently expressed hypotheses in community ecology, there are many possible relationships between phylogenetic relatedness and co-occurrence that can be tested using divergence metrics. Gerhold et al. ( 2015 ) provide alternative scenarios that may preclude the interpretations described above – for example, trait similarity may actually facilitate coexistence (see also Mayfield & Levine, 2010 ), competitive exclusion may be incomplete in assemblages, and regional species pools and processes, rather than local processes, may determine local assembly. Thus, testing questions about evolutionary history requires both identifying the correct type of metric for a given question as well as considering the assumptions that might relate patterns to processes.

The phylogenetic topology of species' assemblages can further provide information about processes structuring regional species pools (Heard & Cox, 2007 Purvis et al., 2011 ), and the likelihood that these will be invaded or altered (Gerhold et al., 2011 ). Macroecological studies have incorporated information about divergences in phylogenies to compare phylogenetic distances separating sister lineages and capture variation in diversification rates (e.g. Weir & Schluter, 2007 Ackerly, 2009 ), to identify geographical centres of diversification (e.g. Jetz et al., 2012 ), or the drivers of niche evolution or conservation (e.g. Wiens & Donoghue, 2004 Dormann et al., 2009 ). Such macroecological approaches allow tests of whether diversification rates differ between biogeographical regions, across latitudes or at different times through history. In addition, patterns can be compared to null expectations generated from models that integrate the processes of speciation, extinction and colonization (Pigot & Etienne, 2015 ) providing more powerful tests of the mechanisms structuring regional species assemblages.

(3) Applying regularity metrics

The regularity metrics appear less frequently in the literature, and we identified no published examples for β-diversity. They are typically used for questions about how evenly evolutionary history is distributed between taxa in an assemblage, and as with divergence metrics, are often applied with the assumption that phylogenetic distance is a proxy for differences in functional traits. Under such a framework, one might hypothesize that greater evenness in the distribution of similarity among species should result in lower competition (Kraft, Valencia & Ackerly, 2008 ). Cadotte ( 2013 ) manipulated phylogenetic relatedness and species richness in plant communities, and tested whether the selection effect (the dominance of highly competitive or productive species, one putative mechanism underlying the diversity–ecosystem function correlation), might be related to the topology of the phylogenetic tree. In fact, the selection effect was correlated with a regularity metric, the imbalance in abundances among clades (IAC). As regularity metrics reflect evenness in the distribution of dissimilarity among species, this finding suggests that the selection effect is strongest when closely related species are present. In the field of macroecology, Davies & Buckley ( 2011 ) considered VPD to explore unevenness in the distribution of PD globally for terrestrial mammals, which provided insight into the historical processes behind global patterns of species richness.

5-Year Impact Factor

What does it measure? The 5-Year Impact Factor is a modified version of the Impact Factor, using five years’ data rather than two. A journal must be covered by the JCR for five years or from Volume 1 before receiving a 5-Year Impact Factor.

How is it calculated?

Number of citations in one year to content published in Journal X during the previous five years, divided by the total number of articles and reviews published in Journal X within the previous five years.

What are its limitations? The 5-Year Impact Factor is more useful for subject areas where it takes longer for work to be cited, or where research has more longevity. It offers more stability for smaller titles as there are a larger number of articles and citations included in the calculation. However, it still suffers from many of the same issues as the 2-year Impact Factor and those common to all citation metrics (see above).

SISR: System for integrating semantic relatedness and similarity measures

Semantic similarity and relatedness measures have increasingly become core elements in the recent research within the semantic technology community. Nowadays, the search for efficient meaning-centered applications that exploit computational semantics has become a necessity. Researchers, have therefore, become increasingly interested in the development of a model that can simulate the human thinking process and capable of measuring semantic similarity/relatedness between lexical terms, including concepts and words. Knowledge resources are fundamental to quantify semantic similarity or relatedness and achieve the best expression for the semantics content. No fully developed system that is able to centralize these approaches is currently available for the research and industrial communities. In this paper, we propose a System for Integrating Semantic Relatedness and similarity measures, SISR, which aims to provide a variety of tools for computing the semantic similarity and relatedness. This system is the first to treat the topic of computing semantic relatedness with a view of integrating different key stakeholders in a parameterized way. As an instance of the proposed architecture, we propose WNetSS which is a Java API allowing the use of a wide WordNet-based semantic similarity measures pertaining to different categories including taxonomic-based, features-based and IC-based measures. It is the first API that allows the extraction of the topological parameters from the WordNet “is a” taxonomy which are used to express the semantics of concepts. Moreover, an evaluation module is proposed to assess the reproducibility of the measures accuracy that can be evaluated according to 10 widely used benchmarks through the correlations coefficients.

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Results and discussion

To evaluate the performance of our MTL approach, we performed MTL on the level of all groups and classes of drug targets, building a model simultaneously for all drug targets within that group or class. We only considered groups or classes that have more than one drug target, because otherwise there would be no difference with STL, and only included drug targets for which the minimum size of their dataset was 10, because we employ tenfold cross-validation. In other words, each dataset must contain at least 10 compounds with their corresponding activity against that drug target.

We compared the three settings discussed in “Methods” section by running MTL on all drug classes and groups, obtaining a list of drug targets with their corresponding RMSE values for STL, feature-based and instance-based MTLs. Finally, we counted the number of cases where each setting had lowest RMSE.

To examine the distribution of RMSE values for each setting we drew histograms, ran Shapiro–Wilk tests [55], generated Q–Q plots, and concluded that these values do not follow a normal distribution. Hence, we applied the non-parametric Wilcoxon Signed-ranks test to examine whether or not the difference between these values is statistically significant. For each experiment, we show the results of three different Wilcoxon Signed-ranks tests to pairwise compare the RMSE performance of the three settings. The following subsections show the details of our experiments using ChEMBL’s 6-level hierarchical classification and its grouping by preferred names.

Using ChEMBL’s class levels

We previously described ChEMBL’s 6-level hierarchical protein family classification which starts with L1 (most generic class) to L6 (most specific class). Table 5 displays the number of classes we obtained at each level. Note that Table 5 shows the number of classes at each level in the hierarchy explained in “Drug target classes” section, and this is different from the number of groups in the preferred named grouping explained in “Drug target groupings” section.

Broad classes such as enzyme and membrane receptors can be found at L1, whereas as we traverse down the hierarchy, we can find more specific classes such as antiporter and protein kinase at L3 and amine and motilin receptor at L5. It is reasonable to assume that more specific classes are more evolutionarily related. L5 has more classes than any level, i.e. 180, as shown in Table 5. Over the total of 1091 drug targets (corresponding to 1091 assays we run experiments for), we expect that a grouping at L5 would yield sets of targets which are closely related. Therefore, we present our experimental results using this level.

Table 6 shows a simple sign test where we count how many times the RMSE value for each algorithm is less than the other. The +ve column indicates how many times the RMSE for the first setting is less than the second setting while the −ve column indicates how many times the RMSE for the second setting is less than the first setting. This shows that, for instance, feature-based MTL outperforms STL in 686 of the cases. Counting the number of overall wins, shown in Fig. 1, yields that instance-based MTL outperforms both feature-based MTL and STL on 741 drug targets. Feature-based MTL won on 179 occasions and STL performed best on 171 occasions. The statistical significance of these results is shown in Table 7. Finally, Fig. 2 shows a point ranking where we award the best setting three points, the second best two points and the third best one point.