A Broken Silence: At the end of the day – Official Music Video (Censored)
“At the end of the day” is the second single from a broken silence’ new LP “Soul”, set for release 29th November.
Performance camerawork by Alex Botton’, video by a broken silence, with special thanks to ABC (listed ABS submissions). Edit by Daniel Bartulovich & Rosie Chmielewski
Lyrics
At the end of the day I’m losing…
I tried to build up a legacy, so no one’s forgetting me, I wasted my energy, now no one has empathy. I’ve been at it since 17, it cost me my family, we could never live happily, I was lost in my apathy. Looking back where it started at, for years working hard as I can… Stress on my cardiac… the cause for my heart attack. No investment in family, I’m left with insanity, fake friends have abandoned me, time to pay for my vanity. Fuck this hospital bed, I’m better of dropping dead. Dump a whole lot of meds… I’ve dug a hole of regret now. Now tell my kids “live their dreams”… cause they aren’t talking to me; doctor please… can you make a call to Terrace? Fall on my knees. I wish I could talk to the old me… Go back and say to the old me…
At the end of the day I’m losing. All I ever want’s right here, right now.
At the end of the day I’m losing. All I ever want’s right here, right now.
Gave my kids everything, it was not to their benefit, they needed my presents, now they’re seeing a therapist. I showed no affection to the daughter I love.
I never gave up a second to kick a ball with my son. Gotta get out of this bed and rip off these plugs, I’m better off dead, let them fight over my funds…
cause when it’s all said and done, I just wish I could breath, smoke in my lungs. They can’t fix my disease, fall on my knees… I wish I could talk to the old me… Go back and say to the old me…
And all I ever wanted was to build up a legacy… there’s poison in my memories now. And all I ever wanted was to build up a legacy… there’s poison in my memories now. And all I ever wanted was to build up a legacy…
At the end of the day… And all I ever wanted was to build up a legacy… its poison in my memories now.
At the end of the day… And all I ever wanted was to build up a legacy… its poison in my memories now.
Inequality is a general feature of human and non-human animal societies. Most societies exhibit disparities in individual access to resources, physical condition and social relationships. These disparities can be conceptualized as dimensions of wealth inequality, which translate into differences in outcomes such as health, longevity and reproductive success, and ultimately influence variation in fitness. Wealth inequality in different dimensions may be driven by similar underlying processes and have shared effects on outcomes. Social systems may also differ in which dimension of wealth most directly influences individual outcomes. An overarching study of the causes and consequences of wealth inequality facilitates comparisons of the mechanisms underlying variation in outcomes in various societies. Such a perspective can interrogate the myriad potential factors that generate and maintain wealth inequality, scrutinize the consequences of wealth inequality in terms of individual health and reproductive outcomes, or investigate how inequality changes across time within a society.
Researchers in both human- and animal-oriented fields are motivated to understand how wealth inequality arises, is sustained and acts as a mechanism underlying disparities in outcomes, but the general emphasis differs across fields. In the study of modern human societies, research often focuses on how wealth inequality influences health and well-being, with the aim of informing policies that reduce disparities and promote the well-being of as many people as possible. Research in evolutionary anthropology and related fields examines the role of inequality in human evolution, including the evolutionary origins of human societies and the effects of inequality on fitness in humans [1–7]. In studies of animal societies, the focus often takes an explicitly evolutionary biology perspective, focusing on wealth inequality as a mechanism that generates variation in fitness.
Wealth, inequality and their influences on fitness variation have been considered in different contexts within the fields of evolution and ecology. For instance, a century of work has explored how networks of dominance relationships arise from interactions among group-mates and influence social structure and fitness-related outcomes [8]. Sexual selection theory addresses the causes and consequences of inequality in mating success [9], and studies of reproductive skew examine behavioural constraints on inequality in reproduction [10,11]. Research into collective decision-making explores the causes and consequences of inequality in behavioural decisions [12–14]. Woven into these subfields are theories of kin selection and multilevel selection, which seek to identify how individual wealth influences the indirect fitness of other individuals, and how inequalities within and between groups influence evolution. Thus, much work on social evolution has concerned itself with the causes and consequences of wealth inequality, albeit without explicitly referring to the parallel concepts of wealth and inequality that human-oriented fields have more thoroughly explored. Notable exceptions are work on privatization and property by Strassman & Queller [15] and intergenerational wealth transfer by Smith et al. [16]. In this paper, we expand on this prior work to provide a more overarching review of the concepts of wealth and inequality in animal societies, and explore how wealth inequality can be a source of social selection [17–19].
Here we present a research agenda for studying wealth inequality within an ecological and evolutionary context. We synthesize concepts, questions and empirical insights from research in animals and humans to investigate the ecological and evolutionary implications of inequality. We show that this ‘ecology of wealth inequality’ approach presents the opportunity to clarify the role of social systems as central to evolutionary biology, and to reintegrate key evolutionary concepts that have often been perceived as alternatives (e.g. trait evolution, niche construction, extended phenotypes) as different dimensions of the wealth–fitness relationship. We identify four key opportunities in the ecological study of inequality: (i) developing measures of wealth and inequality as taxonomically broad features of societies, (ii) considering how feedback loops link inequality to individual and societal outcomes, (iii) exploring the ecological and evolutionary underpinnings of what makes some societies more unequal than others, and (iv) studying the long-term dynamics of inequality as a central component of social evolution. In each section, we review existing work and highlight areas requiring additional empirical and theoretical attention. We aim to motivate a cohesive interdisciplinary approach to understanding inequality as a widespread and diverse biological phenomenon.
2. What are wealth and inequality in animal societies?
Non-humans do not have bank accounts, so how can they be wealthy? Economists and evolutionary anthropologists have long known that wealth can take many forms [20,21]. Wealth manifests in many currencies, or quantities of attributes or possessions that impact an individual’s access to ‘valued goods and services’ [22]. Although the currencies of wealth are numerous, they can be pooled into three superseding categories (here ‘aspects’; figure 1, top left) [4,22,23]. Material wealth denotes extrasomatic currencies such as money, land or livestock. Relational wealth consists of social connections, often measured as ties in a network of relevant social interactions or relationships such as food sharing, prestige or cooperative hunting. Finally, embodied wealth refers to attributes of individuals, such as size, strength or knowledge.
Figure 1. A schematic of the ecology of inequality. Centre circle: inequality describes the distribution of wealth among individuals, which can be measured using metrics borrowed from economics (box 1). Top left: wealth is taxonomically broad and occurs in many currencies, grouped into three aspects. Top right: inequality emerges from individual wealth through bottom-up causation and has a top-down influence on individual outcomes, both directly and via its effects on group outcomes. These effects are independent of the effects of wealth, but can feed back to influence wealth and inequality. Bottom left: multiple ecological (e.g. food/water distribution) and behavioural (e.g. wealth inheritance) processes are hypothesized to influence the amount of inequality in societies, but it is less clear at what scale this influence occurs or to what degree these processes operate across species. Bottom right: inequality is dynamic. Active and passive processes produce changes in wealth within an individual’s lifetime and across generations, leading to typical wealth trajectories over the lifespan. The amount, timing and direction of wealth trajectories are expected to exert selection on individuals to optimize their experienced costs and benefits of sociality. (Online version in colour.)
Figure 1. A schematic of the ecology of inequality. Centre circle: inequality describes the distribution of wealth among individuals, which can be measured using metrics borrowed from economics (box 1). Top left: wealth is taxonomically broad and occurs in many currencies, grouped into three aspects. Top right: inequality emerges from individual wealth through bottom-up causation and has a top-down influence on individual outcomes, both directly and via its effects on group outcomes. These effects are independent of the effects of wealth, but can feed back to influence wealth and inequality. Bottom left: multiple ecological (e.g. food/water distribution) and behavioural (e.g. wealth inheritance) processes are hypothesized to influence the amount of inequality in societies, but it is less clear at what scale this influence occurs or to what degree these processes operate across species. Bottom right: inequality is dynamic. Active and passive processes produce changes in wealth within an individual’s lifetime and across generations, leading to typical wealth trajectories over the lifespan. The amount, timing and direction of wealth trajectories are expected to exert selection on individuals to optimize their experienced costs and benefits of sociality. (Online version in colour.)
This framework reveals how animal societies are also structured by multiple dimensions of wealth. These same three aspects—material, relational and embodied wealth—are key elements of animal societies and map clearly onto established concepts in ecology and evolution, such as constructed/defended niches, social niches and phenotypic traits. Material wealth currencies include defendable resources such as food items, nest sites and territories, as well as ‘constructed’ resources such as food caches, shelters and nest decorations [15,16]. For instance, material wealth is prominent in acorn woodpeckers (Melanerpes formicivorus), which invest heavily both in granary construction (the work of generations of woodpeckers) and in the collection and storage of acorns within the granary [24]. Material wealth may also take the form of empty snail shells occupied by hermit crabs (Pagurus longicarpus)—resources that are unequally distributed in quality and directly affect fitness outcomes [25]. Relational wealth describes an individual’s social niche [26], encompassing social relationships and interactions such as grooming, huddling or dominance. Considerable evidence points to the impact that relational wealth has in human and non-human animal societies [6,27,28]. For example, social alliances influence rank and fitness in spotted hyenas (Crocuta crocuta) [29]. Embodied wealth is made up of phenotypic currencies such as body size, fat reserves, sperm quality, ornament size, display quality or information. Classic examples of embodied wealth are condition-dependent signals, such as the male house-finch’s (Carpodacus mexicanus) bright red plumage [30]. These different aspects of wealth operate concurrently, and biological market theory provides a framework for understanding exchanges in a wealth of different currencies [31].
Wealth inequality describes the spread and skewness of distributions of wealth (figure 1, centre circle) in these different dimensions (box 1). The scale at which inequality is assessed can be tuned flexibly according to the question and the study species. For instance, one can measure inequality among individuals in a society or social group, or among individuals in a population consisting of multiple social groups. When wealth operates at the group level (e.g. group territories, shared food caches), wealth inequality among groups can be assessed at the population level.
Box 1. Measuring inequality.
Here, we provide a brief introduction to the methods for measuring inequality, intended to introduce the reader to what is an extensive body of literature in economics. Distributions can differ from pure equality in numerous ways [32–35]. When empirical wealth distributions are well described by the functional form of one or more distributions, inequality can be described analytically via the parameters specifying the distribution [36]. Alternatively, inequality can be measured by summarizing the amount of wealth held by individuals in a certain quantile (e.g. the proportion of total wealth held by the wealthiest 10% [37]) or by comparing the wealth of individuals in different quantiles. Finally, ‘index’ approaches summarize inequality into a single numerical index. The Gini index is the most commonly used metric of inequality, and although most often applied to income, it has also been used to study inequality in distributions of monetary wealth [38], land ownership [23], faculty production by universities [39], body size [40], plant sizes [41] and hermit crab shell sizes [25]. Because a single parameter cannot fully summarize the shape of a distribution, different indices are sensitive to different features of unequal distributions, so caution is warranted when indices disagree [32]. Finally, it is important to note that most of these methods were developed to describe inequality in large nation-states, and methodological challenges remain to facilitate comparative approaches to inequality in smaller societies such as those found in non-human systems [34,35,42,43].
There is broad consensus in evolutionary theory that material and relational wealth (i.e. constructed and social niches) can influence fitness, drive adaptation and contribute to evolutionary change [44]. Existing biological concepts also describe the transmission of wealth across generations via mechanisms of genetic and epigenetic inheritance, ecological inheritance [45] and social inheritance [46]. Intergenerational transmission of wealth may affect ‘privilege’ as a source of inequality in animal societies [16]. Exploring evolutionary themes such as niche construction and social inheritance from the lens of wealth inequality could provide clarity to debates on how to integrate these dynamics in evolutionary theory [47,48]. Specifically, we argue that the patterns of distribution of each aspect of wealth matter, and understanding the structural properties of wealth inequality is key to evolution. For example, niche construction may play a key role in evolution only when the intergenerational transmission of material wealth fundamentally alters how fitness is related to embodied aspects of wealth.
3. What are the consequences of inequality?
Inequality can influence outcomes for individuals directly or by impacting group outcomes (figure 1, top right). There is a long history of sociological research describing different types of effects of wealth inequality (reviewed in [49]). Most directly, variation in individual wealth may translate into variation in outcomes, and such effects may be linear or nonlinear. From an evolutionary ecology perspective, simple effects of wealth on fitness represent selection on various aspects of wealth, such as traits (embodied wealth), resource acquisition and defence (material wealth), or social behaviour (relational wealth). However, sociological approaches to wealth inequality also reveal other effects that may be relevant to non-human societies. On top of simple wealth effects on outcomes, individuals are influenced by inequality in the distribution of wealth such that two equally wealthy individuals living in societies with different levels of wealth inequality might experience divergent outcomes. Here, we highlight three such effects: (i) the overall level of inequality at the group or society level may have effects beyond an individual’s wealth; (ii) behavioural responses to inequality, and (iii) effects of inequality on group persistence or collective action.
Wealth and wealth inequality impact individual health and well-being [28,50–52]. In humans, more unequal societies are often associated with negative individual and societal outcomes [53,54]. An evolutionary comparison across primates, including humans, reveals that life-expectancy increases with lifespan equality, further indicating that inequality covaries with individual outcomes [55]. Inequality negatively impacts health and well-being through behavioural changes [56] or psychosocial stress [57]. In humans, inequality-induced stress is more extreme in societies that are more unequal, even for individuals of high social status [58]. Status-induced stress can affect both low- and high-wealth individuals, and who experiences most stress can depend on the dynamics of the social system [51,59,60]. Overall, widespread association between wealth inequality and individual outcomes supports the hypothesis that living in the context of wealth inequality is a ‘fundamental cause’ of a suite of negative outcomes [28,56,61].
Individuals attend to inequality within their societies and alter their behaviours accordingly. Experiments in primates, corvids and domestic dogs suggest that the perceived value of a resource is influenced by an individual’s observations of the value of the resources their group-mates receive [62]. Individuals often then alter their social behaviour, for example by punishing individuals that receive the higher valued resource [63]. Similarly, subordinate queens of Polistes fuscatus wasps greatly increase aggression towards dominants when they perceive that dominants are claiming too unequal a share of reproduction [64]. In humans, an individual’s wealth influences their perceptions about the degree of inequality in society [65] and their status-seeking behaviour [66]. In many species, individuals use social information about their status relative to their competitors when making decisions about how and with whom to compete [67]. In sum, intra-group competition and inequality are linked by a feedback loop involving individual perception of their own social status, the social status of others and the amount of inequality in the group. To understand this feedback loop, we should continue to explore how individuals perceive inequality, and how their response to inequality affects social structure. Systems where signals of wealth can be manipulated independently of actual wealth provide a means to experimentally manipulate perceived inequality.
Inequality can influence group outcomes such as group persistence and collective action. Reproductive skew theory [10,11] addresses how inequality in reproduction can affect the productivity or persistence of the group. Inequality can also influence a group’s ability to cooperate or achieve collective action. In cooperation experiments with chimpanzees (Pan troglodytes), bonobos (Pan paniscus) and cotton-top tamarins (Saguinus oedipus), evidence suggests that species that divide the rewards of cooperation more equally are more likely to show cooperative behaviour [68,69]. Theoretical and empirical studies of collective action problems (e.g. public goods game) suggest that inequality has complex and often unpredictable effects on cooperative behaviour [70–77]. However, a rough pattern emerges in the literature suggesting that the effect of inequality on cooperation might depend on the type of wealth under consideration. In studies where individuals vary in the resources they can invest in cooperation (i.e. material wealth), inequality typically reduces cooperation [70–72]. However, inequality in social influence can promote cooperation by eliminating free-riders and overcoming coordination challenges [73–77]. Other evidence suggests that inequality can influence group outcomes by improving or impeding the function of groups, for instance by altering costs of coordination, resilience to variable environmental conditions, or ability to compete with other groups [73,75,78,79]. For example, burying beetles (Nicrophorus nepalensis) invest more in cooperation in the face of interspecific competitors [80]. A complex relationship between inequality and environment may explain global patterns in the evolution of cooperation: in both Polistes wasps and cooperatively breeding birds, the evolution of cooperative groups is associated with the environmental conditions that may increase the need for collective action (e.g. unpredictable environments: [81–83]). Overall, the complex results from theoretical studies suggest a need for empirical work on the links between inequality, individual outcomes and group function in animal systems.
4. What are the causes of inequality?
Multiple behavioural and ecological processes have been hypothesized to influence the amount of wealth inequality within societies, but the extent to which these mechanisms explain variation within versus among species is not fully clear (figure 1, bottom left). Some aspects of inequality seem to be relatively flexible, whereas others are more constrained. For example, in a population of olive baboons (Papio anubis) in Kenya, a mass mortality event prompted a long-term shift towards a more tolerant society with more equally distributed stress burdens, perhaps as a result of the death of the individuals that competed most intensely for high status [84]. However, a comparative network motif analysis of dominance hierarchies across many species suggests strong constraints on their structure related to transitivity of dominance relations [85]. Furthermore, in macaques, a suite of behaviours related to inequality in within-group conflict covary across species, producing macaque societies with different ‘social styles’ and suggesting potential phylogenetic constraints on wealth inequality [86,87]. More longitudinal and phylogenetic studies will be crucial to advance our understanding of plasticity and constraint in inequality across species.
What behavioural and ecological mechanisms influence variation in inequality within and among species? Ecological conditions—such as the patchiness, density and defensibility of resources—have long been hypothesized as a driver of material wealth inequality [1,2,9,88] (but see [89,90]). Additionally, inequality may be influenced by behavioural traits such as levelling coalitions used to control would-be dominants [91], aversion to unequal payoffs [62], preferences regarding perceived inequality [92], status-seeking behaviour [93], visibility of wealth [94] and cognitive processes relating to social competition [67]. Individuals can actively suppress the wealth of others, as is seen in growth suppression by many fish [95] or the interruption of social bond formation in ravens (Corvus corax) [96], or subordinates may voluntarily reduce their own wealth to avoid conflict with group members [97]. Self-reinforcing dynamics—where ‘rich-get-richer’ feedbacks lead wealthy individuals to gain more wealth—can also influence the amount of inequality in societies [98] (see §5). Finally, these behavioural and ecological mechanisms interact. For example, the evolution of male coalitions in primates is explained by resource defensibility [99], and in vulturine guineafowl (Acryllium vulturinum), monopolization of clumped resources by dominants can lead to more egalitarian group movement decision-making [13].
Although drivers of inequality may differ among species or wealth aspects, some hypothesized causes of inequality are expected to operate across contexts. For example, the social transfer of wealth is one hypothesized driver of inequality that is likely to operate widely [3,4,16]. In a broad survey of human societies with diverse production systems, the increased fidelity of intergenerational transmission of wealth was associated with more extreme inequality [4,22]. In non-human animals, social inheritance of territory [100,101], knowledge [102,103], social relationships [46] and food caches [24] could provide ample contexts in which to test this hypothesis in diverse systems [16]. For instance, the social inheritance of dominance status in spotted hyenas and Old-World primates may drive inequality in dominance among lineages [29]. In fact, the widespread transmission of wealth across generations points to the evolutionary importance of non-genetic inheritance [45] and selection in response to multigenerational processes [104]. Another broadly operating hypothesized driver of inequality is intergroup conflict. When unequal groups are more effective or willing competitors, selection for success in intergroup conflicts can lead to increased within-group inequality in influence during collective action [79,105,106], and these leaders can also use their influence to increase inequality in other dimensions of wealth [107]. Here there is potential for positive feedback when the individuals that benefit most from intergroup conflict are also effective initiators of these conflicts, as seen in humans and banded mongoose (Mungos mungo) [108,109]. Finally, environmental stressors arising from climate change are expected to impact many species, highlighting another potentially broadly acting driver of inequality that we need to better understand. Studying shared processes influencing inequality in diverse wealth currencies and species is key to understanding the evolution of inequality and its role in societies.
5. How does inequality change over time?
Inequality is dynamic: neither the level of inequality nor an individual’s wealth is fixed, and both can change over short or long timescales (figure 1, bottom right). One avenue for understanding these dynamics is through the economic concept of social mobility, which describes the dynamics of wealth measured at the individual or lineage level. Aggregating these measures across members of a social group reveals the society-level tendency for individuals or lineages to gain or lose wealth over time, producing more rigid or fluid societies. By integrating over time, social mobility mediates the link between inequality measured at a given time point and the processes or outcomes occurring over individual lifetimes.
Social mobility can vary in the timescale at which it occurs and the processes by which it arises. Intra- and intergenerational mobility classify the generational scale at which mobility occurs. Intragenerational mobility describes the degree to which individual wealth changes, producing wealth trajectories over the lifespan. Intergenerational mobility refers to the change in wealth within lineages across generations and is the type of social mobility most often studied in humans [110–112]. Examining the correlation between parents’ and offspring’s wealth provides an empirical measure of the extent to which an individual’s position in society is malleable versus predetermined [113]. Increasingly, researchers are expanding the study of intergenerational mobility to include multigenerational effects, such as the effects of grandparents or other more distant kin [114,115].
Processes influencing social mobility can be active or passive: active mobility occurs when an individual’s wealth changes with respect to their group-mates by reversing the wealth-ordering of individuals, whereas passivemobility occurs as a result of demographic processes such as births and deaths [116]. These demographic processes frequently produce gradual changes that have direct and indirect effects on social structure by removing and replacing individuals and altering existing social relationships [117]. In some cases, demographic changes can push societies over tipping points, or precipitous shifts in social structure that can show hysteresis [118]. Revolutions [119], mass mortality [84,119,120], group fissions [121], the arrival or loss of certain individuals [122–124] and expulsions of group members [125] are examples of active and passive processes that could produce precipitous changes. For instance, social perturbation experiments in captive fish, primates and mice demonstrate how removal of high-status individuals can lead to rapid behavioural, physiological and cognitive changes in other individuals [122–124].
The long-term additive combination of social mobility produces long-run inequality, which describes equilibrium patterns of inequality around which a society fluctuates [37,126], assuming such an equilibrium state exists. Understanding where a society sits relative to its expected equilibrium state will require long-term studies in the order of multiple generations. In turn, such work creates opportunities for exploring the forces that lead societies to deviate from or return to their equilibria. This long-run perspective could help us understand when and why societies may have distinctively low social mobility, leading to ‘durable’ inequality [127], or inequality that persists across individuals, time or generations [1]. Durable inequality can give rise to social classes, where individuals of different classes form social networks with different structures, face different mortality sources and cope differently with stressful conditions [60,128,129]. One process producing durable inequality is self-reinforcing dynamics, where already wealthy individuals accrue disproportionately greater wealth [130–133]. Preferential attachment and ‘rich-club effect’ models of social relationships demonstrate how relational wealth can show such self-reinforcing dynamics [134,135]. Frequency-dependent or fluctuating selection may be a counterforce that inhibits the buildup of durable inequality by altering fitness landscapes [136].
Patterns of social mobility may influence the evolution of a wide suite of behavioural strategies such as tolerance and wealth-seeking behaviour, as well as life-history traits related to pace of life (figure 1, bottom right). When upward intragenerational mobility is achieved through active processes, selection is expected to favour individuals that challenge their group-mates, whereas conflict avoidance and tolerance should be favoured in species where upward intragenerational mobility is achieved through passive processes (e.g. social queuing; [137]). Low intergenerational mobility is expected to amplify selection on traits related to intragenerational mobility, as any changes within a generation are likely to persist and influence future generations. This hypothesized selection driven by social mobility reflects ways in which patterns in the dynamics of social structure can feed back to influence the evolution of individual traits [138], including life-history traits.
Contrasting hypotheses about the influence of social mobility on the stability of social groups highlights potential tradeoffs in the evolution of social structure. On the one hand, some have suggested that upward social mobility is crucial for long-term group stability, as individuals are expected to leave societies where they have no opportunity for wealth acquisition [126]. This pattern of upward mobility is prominent in societies where individuals ‘queue’ for wealth, such as in long-tailed manakins (Chiroxiphia linearis) [139], where individuals move up the queue through passive processes (e.g. death of wealthier individuals) [137,139,140]. By contrast, overly frequent active mobility can cause social instability, which is associated with negative consequences for individuals and societies [51,141–143]. These contrasting perspectives emphasize the need for theoretical and empirical work that generates and tests hypotheses about the link between social mobility and the functioning of societies in diverse species.
6. Conclusion and future directions
A key question in ecology and evolution is how the structure of groups arises and impacts the individuals that compose them [138]. Inequality in the distribution of wealth—be it relational, material or embodied—is a group-level feature that is hypothesized to impact individual and group outcomes. Here we coalesce disparate studies of inequality in biological systems into a research framework addressing inequality across ecological and evolutionary contexts and identify three overarching research foci.
First, how does inequality impact individuals beyond the simple effects of individual wealth? Evidence suggests that individuals attend to the amount of inequality within their societies, and that inequality per se may have adverse effects for individuals. Here, theoretical work has outpaced empirical work, and examining the impacts of inequality on individual and group outcomes in non-human systems will be fruitful. Experimental studies of inequality in laboratory populations is a promising tool for disentangling the effects of inequality from the effects of wealth. The recent surge in work on social dimensions of health and lifespan in non-human animals promises to shed light on potential avenues by which inequality influences fitness [28].
A second broad aim of the ecology of inequality is to understand the forces that cause inequality, both in the short term and at evolutionary timescales. Some aspects of inequality can be plastic—even sensitive to the behaviour of a single individual—whereas other aspects of inequality are evolutionarily constrained. The interplay between behavioural processes and environmental conditions (e.g. resource scarcity and competition) fundamentally shapes wealth inequality. Biogeographical and phylogenetic approaches may be useful here for identifying ecological and evolutionary patterns in wealth inequality at a global scale. Finally, feedback loops operating across species and types of wealth might explain why inequality is such a common feature of societies across the animal kingdom.
Third, it is crucial to take a dynamical perspective on inequality to understand selection on individual traits, long-term patterns in inequality, and the stability and persistence of groups. Social mobility—or changes in wealth—can occur owing to various processes and at different timescales, leading to higher-order patterns in inequality among individuals and their descendants, such as social classes or family dynasties. However, very little is known about the existence or implications of these higher-order patterns in inequality in non-human systems. Long-term studies that track groups and their constituents over multiple generations are uniquely situated to address this knowledge gap. Furthermore, we call for theoretical models that explore how lifetime patterns of social mobility impact the evolution of life-history traits and wealth-seeking behaviour.
Inequality is a curiously widespread feature of societies. The framework presented here offers a way forward for exploring the causes of inequality, its impacts on individuals and its role in social evolution. The framework allows inequality to be understood in specific contexts while also providing a means for comparative insight and the identification of general features of inequality operating across species and dimensions of wealth. This approach at once strengthens biological and sociological fields by integrating perspectives and facilitating the exchange of ideas, paving the way for new insights into ecological and evolutionary forces impacting social organisms.
Data accessibility
This article has no additional data.
Authors’ contributions
E.D.S.: conceptualization, writing—original draft, and writing—review and editing; D.S.: conceptualization, writing—original draft, and writing—review and editing.
Both authors gave final approval for publication and agreed to be held accountable for the work performed herein.
Conflict of interest declaration
We declare we have no competing interests.
This work was supported by the University of Nebraska-Lincoln Population Biology Program of Excellence, NSF Grant OIA 0939454 via ‘BEACON: an NSF Center for the Study of Evolution in Action’, and the Alexander von Humboldt Foundation.
Acknowledgements
Thanks to Monique Borgerhoff Mulder, Mauricio Cantor, Danai Papageorgiou, members of the UNL School of Biological Sciences Behaviour Group, three anonymous reviewers and the reviews editor, Innes Cuthill, for helpful comments on prior versions of this manuscript.
Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.
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A jam-packed year of events saw Brazil take home back-to-back Majors, the creation of ELEAGUE and CS:GO on television, and much more.
As a new year was ushered in, so too were a number of changes to the CS:GO landscape as the game began to seriously pick up steam, gaining in both player base and interest in its esports scene. Prize money burgeoned with ESL announcing that Pro League would increase its pay out by $500,000 to $1.5 million, and DreamHack revealing that they would host two $250,000 events during the year, dubbed “DreamHack Masters”, with the first one taking place in Malmö, Sweden.
Both organizers were making a concerted effort to compete against Turner and WMG | IME’s ELEAGUE ahead of its debut season, with the televised tournament boasting a mind-boggling prize fund of $2.5 million over the course of the year. Valve also kept up with the growing money on offer, quadrupling the pay out from the Major to a mammoth $1,000,000.
Blooming interest in the competitive scene — thanks in part to the notoriety of Turner creating ELEAGUE, but also through organically growing viewership — led to slew of notable organizations pick up rosters, including NRG, Gambit, Echo Fox, and FaZe. The former TSM members also formed Astralis and added Danny “zonic” Sørensen as coach — a historic moment as the organization was one of the first to have shares owned by the players — and transparency was attempted as Nicolai “device” Reedtz and Andreas “Xyp9x” Højsleth both spoke publicly about offers they had received and what their salaries would be for 2016.
fnatic extend winning streak to six
Two key signings would also go on to have a substantial impact in the coming months and years, one being Ninjas in Pyjamas‘s signing of Björn “THREAT” Pers as coach, and another with Cloud9 adding Jake “Stewie2K” Yip. Aleksandr “s1mple” Kostyliev also made a surprise move to Liquid, heading over the water as the North American side made an effort to tame the Ukrainian superstar’s hot temperament after he had a tumultuous 2015. None of those teams would attend the first big international LAN of the year though, where a Dennis “dennis” Edman-powered fnatic remained in top shape as they took home the crown. The Swedish side had been on a streak of three LAN victories coming into the start of the season and managed to make it four, narrowly beating Luminosity in the semi-finals before easing past Natus Vincere to win the StarLadder i-League StarSeries XIV Finals.
Simultaneously, the regional Minors for MLG Major Columbus played out as teams booked spots in the Main Qualifier, and to cap off the month, Natus Vincere took home a title of their own, winning DreamHack Leipzig over a Luminosity who were truly hitting their stride, voraciously fighting for trophy contention on their own. Championships wouldn’t come to Gabriel “FalleN” Toledo‘s men quite yet however as fnatic extended their streak of consecutive tournament wins to six, winning ESL Expo Barcelona and IEM Katowice 2016, denying Luminosity the win in a 3-0 grand final in Poland.
fnatic denied LG a win in Katowice
The Brazilian reign of terror begins
It was at the end of March that the Brazilian side finally broke their streak and surged to victory, winning one of the most important tournaments of the year. As the first CS:GO Major in North America and the first to boast Valve’s upgraded prize pool of $1,000,000, MLG Major Columbus was able to deliver on all accounts: it featured a heroic run from a s1mple-powered Liquid on home soil, a team who finally broke their streak of group stage exits at Majors and made it to the semi-finals only to end up encountering a Luminosity that had the indomitable pairing of Marcelo “coldzera” David and FalleN in top form.
Even so, the North American team had LG up against the ropes in the series, leading 15-9 on Mirage and 15-6 on Cache; however, an enormously lucky and game-changing jumping double AWP kill from coldzera went down in the history books and kicked off a comeback on the former map, while on the latter Liquid crumbled entirely as the Brazilian team’s grit and tenacity saw them overcome the odds and claw back to win in overtime, pulling off a series victory in two maps. The grand final, conversely, had much less to write home about, with Natus Vincere pushing Luminosity to the limit on Mirage before the FalleN-led side won in overtime and then demolished on Overpass to become the first non-European team to lift a Major trophy.
As play moved into April, Luminosity briefly faltered, the first big tournament after the Major at DreamHack Masters Malmö seeing them exit in 9-12th place following a shock loss to TYLOO. The Asian team surprised opponents at the event with their stylistic differences on some maps, including their passive approach to defending on Cobblestone, but it wasn’t something that could help carry them through the playoffs. Instead, it was Ninjas in Pyjamas who reigned supreme, breaking a nearly two-year long trophy drought and winning their first big title since ESL One Cologne 2014. The Swedes had been struggling to compete against the growing tactical prowess of other squads across the scene, but the addition of THREAT was already bearing fruit, helped by the fact that coaches could still talk at all times during the match.
The month also saw FACEIT announce the creation of the $3.5 million Esports Championship Series, their own league in partnership with Twitch that would look to compete with ESL Pro League and offer teams co-ownership and a share of the league’s revenue. The competitive map pool also saw a change as a reworked version of Nuke, which had arrived in the reserves pool in February, was moved into the active rotation, replacing Inferno. s1mple also announced he would depart Liquid, and FaZe made their first roster change since picking up their new roster, replacing Mikail “Maikelele” Bill with Fabien “kioShiMa” Fiey. Most importantly though, Olof “olofmeister” Kajbjer took a temporary leave of absence from fnatic due to a wrist injury, and remained sidelined until June, returning for the team’s ELEAGUE group.
Luminosity took charge from there, bouncing back from their defeat in Sweden as they added two trophies to their cabinet with wins at DreamHack Austin and ESL Pro League Season 3 Finals. The former proved a relatively easy event for the Brazilian side, while the latter required FalleN‘s side to recover from a loss in their group stage opener by beating Astralis, OpTic, and Ninjas in Pyjamas en route to the grand final. There, it took all five maps for LG to overcome a resilient G2, an Inferno decider going into overtime before the Brazilian side came out on top, thanks in part to a 125-84 K-D and stellar individual form from coldzera.
After an intense best-of-five series, LG lifted their third trophy of the year
ELEAGUE begins, Luminosity complete transfer to SK
The end of May brought about the start of ELEAGUE, which dramatically raised the bar for a CS:GO tournament in both broadcast and player hospitality. The 24-team competition would continue to play out over the next two months, utilizing a round-robin group stage at first, and it was one where Luminosity remained a dominant force as they had flawless showing in Group A. While teams took part in the first stage of the league, two other tournaments took place, with G2 claiming revenge over LG in the inaugural ECS Finals while another Brazilian team, featuring João “felps” Vasconcellos and Teles twins Henrique “HEN1” Teles and Lucas “LUCAS1” Teles took home a title of their own at DreamHack Open Summer.
Meanwhile, further changes were introduced to the game by Valve as they began an overhaul of the weapon sounds in the game, improving the fidelity and overall quality in a change that was implemented over the course of the remainder of the year. The ESL One Cologne Major Main Qualifier also played out, with Renegades‘ elimination bringing an end to in-game leader Chad “SPUNJ” Burchill‘s playing career as he chose to hang up his mouse.
A blockbuster transfer also unfolded just ahead of ESL One Cologne — after a controversial dispute that began in May between the SK and Luminosity organizations came to an amicable resolution, the entirety of Luminosity‘s lineup was transferred to the German organization. The timing of the transfer, taking place mid-way through the ELEAGUE season, resulted in another controversy as both teams were disqualified from the competition by the league commissioner, leaving the best team in the world ineligible to fight for the Season 1 title.
FalleN’s men go back-to-back
The start of July saw ESL One Cologne play out, in which coldzera remained a stalwart presence for his side; a Brazilian Terminator. The ever-consistent rifler was an immovable object and stellar multi-fragger for SK in their tournament run, which saw them ease past G2 and FaZe in the group stage before moving past FlipSid3, Virtus.pro, and Liquid in the playoffs en route to taking home the trophy. Although Liquid were not competitive in the grand final, they achieved their best run in a Major to date by beating Natus Vincere and fnatic in the bracket stage thanks in part to s1mple, who returned as a temporary stand-in and was immortalized with a graffiti when he dropped from heaven on Cache to win a 1vs2 clutch against the Swedish team.
Big news from the SK camp followed not long after the Major, with coach Wilton “zews” Prado departing the lineup in a bid to once again compete as a player by joining Immortals. Unable to take part in the playoffs of ELEAGUE, the Brazilian team were left looking on from the sidelines as Virtus.pro took the crown instead, beating fnatic after first moving past Ninjas in Pyjamas and MOUZ. The first true tournament break then got underway, but not before Valve sent out a cease and desist letter to skin betting platforms, spelling the beginning of the end for markets like CSGOLounge and OPSkins.
Valve cracks down on coaches in-game leading amid roster changes; PEA announced
stanislaw took charge of OpTic toward the end of the season
Natus Vincere also secured the signing of s1mple to replace veteran in-game leader Danylo “Zeus” Teslenko. The Ukrainian organization intended to have coach Sergey “starix” Ischuk be the caller while stacking firepower on their lineup much like other teams had been doing throughout the year, but that project was swiftly cut down in its infancy as Valve announced they would limit the abilities of coaches, allowing them to only talk during tactical timeouts. ESL wasted little time in implementing the change for their events, leaving NAVI without a true captain at their helm in-game, and other organizers followed suit in the following months. Valve also revealed that the next Major would not take place until January, and that only two Majors would take place in each year that followed.
The Professional Esports Association was also announced, an attempt by a number of North American organizations to create their own exclusive league but one that ultimately failed as by December, players from almost every team that were signed up protested via an open letter, and the league drowned under community scorn.
Packed end to the year
Tournament play resumed in earnest as September got underway, with Immortals winning Northern Arena Toronto to kick off the season. When top tier competition returned, a second trophy went the way of Ninjas in Pyjamas, who won the SL i-League StarSeries Season 2 Finals over G2, and just one week later Virtus.pro took home a title of their own, emerging victorious at DreamHack Open Bucharest with wins over Gambit, Heroic, Dignitas, and Cloud9.
SK were finally looking mortal as they faltered in EPICENTER and ESL One New York the following month, although they still managed deep placements in the tournaments and maintained a dominant streak of wins on Train. The loss at EPICENTER hurt even more as it helped Virtus.pro to usurp FalleN‘s side from the top spot in the world rankings, but it was one they quickly got back thanks to their consistent performances at events, managing a runner-up finish to Cloud9 at the ESL Pro League Season 4 Finals in São Paulo and passing through the group stage of ELEAGUE Season 2.
As the year neared its end, the final string of big tournaments took place: OpTic took home the ELEAGUE Season 2 title over Astralis, but the gla1ve-led side managed to claim revenge over the North American upstarts at the ECS Season 2 Finals in dominant fashion, cruising through the event in a foreshadowing of what was to come in the following years. Teams also took part in the Major main qualifier ahead of the ELEAGUE Atlanta Major, which would kick off after the break as the first big tournament in January.
coldzera and FalleN were named the best and second best players of the year by HLTV.org, with two other SK members also making the top 20 list. Despite enjoying a successful 2016 though, the Brazilian team were set to face a stiff challenge in the new year with an Astralis that were on the rise after adding gla1ve — and competition only got more fierce as the calendar flipped to 2017.
New Zealand is an extremely gorgeous location where several vacationers take a trip on a normal basis. There are lots of areas to appreciate hanging as well as treking out around New Zealand.
The economic situation in New Zealand is flourishing due to the quantity of tourist that they obtain every year. They likewise come to see the lots of plants and also pets.
Some of the most impressive instances of vegetation as well as animals have actually been discovered throughout locations of New Zealand. Much more that 3/4 of the vegetation you will certainly see in New Zealand can just be located there.
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Queen Elizabeth II is the leader of New Zealand and also Helen Clark is the Prime Minister. There is a terrific offer of initiative in area to make sure an excellent top quality of life for the individuals of New Zealand.
New Zealand is an extremely attractive location where lots of travelers take a trip on a routine basis. There are several areas to delight in hanging and also treking out around New Zealand. You will certainly additionally discover some extremely unusual wild animals in components of New Zealand to observe. Also with safety steps in location however, there are a number of pets located in New Zealand that are threatened.
