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Biodiversity Metrics in Lifespan Developmental Methodology

Summary and Keywords

In ecological sciences, biodiversity is the dispersion of organisms across species and is used to describe the complexity of systems where species interact with each other and the environment. Some argue that biodiversity is important to cultivate and maintain because higher levels are indicative of health and resilience of the ecosystem. Because each species performs functional roles, more diverse ecosystems have greater capability to respond, maintain function, resist damage, and recover quickly from perturbations or disruptions. In the behavioral sciences, diversity-type constructs and metrics are being defined and operationalized across a variety of functional domains (socioemotional, self, cognitive, activities and environment, stress, and biological). Emodiversity, for instance, is the dispersion of an individual’s emotion experiences across emotion types (e.g., happy, anger, sad). Although not always explicitly labeled as such, many core propositions in lifespan developmental theory—such as differentiation, dedifferentiation, and integration—imply intraindividual change in diversity and/or interindividual differences in diversity. For example, socioemotional theories of aging suggest that as individuals get older, they increasingly self-select into more positive valence and low arousal emotion inducing experiences, which might suggest that diversity in positive and low arousal emotion experiences increases with age. When conceptualizing and studying diversity, important considerations include that diversity (a) provides a holistic representation of human systems, (b) differs in direction, interpretation, and linkages to other constructs such as health (c) exists at multiple scales, (d) is context-specific, and (e) is flexible to many study designs and data types. Additionally, there are also a variety of methodological considerations in study of diversity-type constructs including nuances pertaining theory-driven or data-driven approaches to choosing a metric. The relevance of diversity to a broad range of phenomena and the utility of biodiversity metrics for quantifying dispersion across categories in multivariate and/or repeated measures data suggests further use of biodiversity conceptualizations and methods in studies of lifespan development.

Keywords: intraindividual variability, human development, diversity, emodiversity, context, complexity, flexibility

Formally, diversity describes dispersion of elements across types. Diversity metrics are often used to describe and study population-level dispersion (e.g., racial/ethnic diversity in humans, species diversity in animals). Theories of human development additionally conceptualize and describe individual-level diversity (e.g., Baltes, Lindenberger, & Staudinger, 2006; Fogel, 1993; Ford & Lerner, 1992; Gottlieb, 2007; Lewis, 2000; Sameroff, 2010; Thelen & Smith, 1996; Thompson, 1994). For example, Baltes (1987) suggests that diversity in timing, direction, and order of functional changes throughout the lifespan is a fundamental component of the human experience. Similarly, Lerner (1991, 1996, 2006) considers diversity of person-context interactions as the cause of both interindividual differences and developmental change. Other lifespan developmental theories highlight how developmental processes (e.g., differentiation and integration) produce and/or change diversity (e.g., Baltes, Cornelius, Spiro, Nesselroade, & Willis, 1980; Sameroff, 2010). Together, these theories extend notions of diversity to multiple types of entities both within and across levels of organization, including diversity of people, of relations, of settings, and of times of measurement (i.e., interindividual differences in intraindividual change).

This article illustrates how biodiversity metrics can be used to study diversity-type constructs pertinent to lifespan development. The first aim is to describe propositions of biodiversity theory and the methods used to quantify biodiversity. The second aim considers how diversity-type constructs fit into existing propositions of developmental theory, including a review of how diversity-type constructs are being used throughout the literature and identification of overarching themes from that review. The third aim is to present methodological considerations pertaining to choice of metric. The fourth aim is to discuss what the future might hold for research utilizing diversity metrics in lifespan developmental research.

Biodiversity Theory and Method

Biodiversity Theory

In ecology, biodiversity is the variety among and between living organisms existing within a given space and time (DeLong, 1996; Gerbilskii & Petrunkevitch, 1955; Hubbell, 2001; Lovejoy, 1980; Magurran & McGill, 2011). This is a simple general definition that has garnered elaboration and debate in the field, with no universally agreed upon definition. Rather, it is often the case that the definition is tailored for a particular research context, necessitating researchers to be explicit about their use of the construct (see Swingland [2013] for a more detailed discussion).

Biodiversity Metrics in Lifespan Developmental MethodologyClick to view larger

Figure 1. Two ecosystems depicting high (System A, left) and low (System B, right) diversity based on variety (e.g., number of “petals” in each system) and relative abundance of each species (e.g., relative length of each petal). All figures were created using the ggplot2® package (Wickham, 2009).

The general idea of biodiversity theory is that each species in an ecosystem serves specific functional roles (Begon, Harper, & Townsend, 1996). As such, ecosystem functioning depends on all organisms together. For example, insects provide for plants’ reproduction through pollination and plants provide nectar for insects’ food. Biodiversity also has implications for ecosystem resilience and stability, especially in response to disturbances. The insurance hypothesis, for example, states that in comparison to less diverse ecosystems, more diverse ecosystems have greater capability to respond, maintain function, resist damage, and recover quickly from perturbations or disruptions (Folke et al., 2002; Holling, 1973). Consider the two ecosystems depicted in Figure 1. When biodiversity in a system is high, such as that depicted by System A, many species fulfill functions needed to keep the ecosystem intact, whereas when biodiversity is low, such as in System B, depletion or overabundance of any one species can have severe consequences for the entire ecosystem (Darwin, 1859; Ehrlich & Ehrlich, 1981; MacArthur, 1955). It should be noted though that while ecosystem resilience (i.e., changes in ecosystem functioning after loss of biodiversity) is accepted in theory, the topic is debated. There is much ongoing research on whether resilience effects found in controlled experimental settings generalize to real-world settings (see Balvanera et al., 2006; Hooper et al., 2005), which components of diversity are “good” or “bad” (or have no influence) for ecosystem functioning, (see Hooper et al. [2005] for a review of common ecosystem response patterns, and see Eisenhauer et al. [2016] and Wardle [2016] for more nuance on both sides of the biodiversity-ecosystem functioning debate), and how to conceptualize and quantify ecosystem health in different types of ecosystems (Wicklum & Davies, 1995). While it is important to keep the ongoing debates in mind, this article takes the core principles of diversity theory, including adaptive function, responses to perturbation, and resilience, as given and directly relevant to lifespan development in humans and uses them to generate potentially useful hypotheses about how individuals change.

Biodiversity Method

Measurement of biodiversity is tied to the particular definition of biodiversity used in a given situation but often considers both variety (e.g., number of “petals” in each system in Figure 1) and relative abundance of species in an ecosystem (e.g., relative length of each petal). Measurement proceeds in three steps: (a) defining the space or community to be quantified (e.g., pastoral lands); (b) defining the time span of sampling/collection (e.g., one week, one year); and (c) defining how entities are differentiated (e.g., species types). Data collection is then structured to quantify the total number and relative distribution of entity types across the defined space and time span. For example, Lebrija-Trejos, Bongers, Pérez-García, and Meave (2008) studied development of biodiversity of abandoned agricultural land through even sampling of the woody and succulent plant species across 15 30m × 30m plots of dry tropical deciduous forest in Nizanda, Mexico for two months. Biodiversity was then defined as the variety and relative abundance of species.

Diversity can be quantified in many ways (Magurran & McGill, 2011). Generally, however, diversity is a function, f(), of the proportions, pj, of samples (e.g., plants) obtained from each of j = 1 to m different species (e.g., Mimosa tenuiflora or Mimosa acantholoba),


The various diversity metrics used in ecology and other fields (e.g., study of racial or income inequality) are all similarly structured (i.e., only differ in the specifics of f(pj); see Firebaugh, 1999). For example, in the previous example, the diversity of the deciduous forest in Nizanda was quantified using the popular Shannon’s entropy metric, first used to quantify biodiversity by Margalef (1957). Calculated for each of i = 1 to 15 plots as


where pj is the proportion of observations that belong to each of the j = 1 to m observed species types. Diversity of the m = 141 observed species (maximum possible diversity = 4.95) ranged between 1.36 and 3.34 across plots that differed in years since “agricultural abandonment”—quantifications that supported study of how biodiversity of previously farmed land develops into mature forest after conservation. In the following section, links to lifespan development are made explicit.

Lifespan Developmental Theory and Diversity-Type Constructs

Although not always labeled as such, many core propositions in lifespan developmental theory—entropy (ordiny), uncertainty (certainty), unpredictability (predictability), inconsistency (consistency), lability (stability), plasticity (rigidity), heterogeneity (homogeneity), variety (uniformity), dispersion (concentration), and processes such as growth (senescence) and differentiation (dedifferentiation, integration)—imply intraindividual change in diversity and/or interindividual differences in diversity (e.g., Baltes et al., 2006; Bronfenbrenner, 1979; Ford & Lerner, 1992; Lerner & Castellino, 2002; Nesselroade, 2001; Sameroff, 2010; Thelen & Smith, 1996; van Geert, 2011).

Intraindividual Change in Diversity

Biodiversity Metrics in Lifespan Developmental MethodologyClick to view larger

Figure 2. Depiction of diversity at micro and macro time scales. At the micro time scale, repeated measures are represented by the high-diversity ecosystem (left) and the low-diversity ecosystem (right). At the macro time scale, intraindividual change in diversity across the lifespan is represented by trajectories including growth (purple dot-dash), decline (blue dash), and differentiation/dedifferentiation (pink solid, green dot) depending on the domain (e.g., socioemotional, self, cognitive, activities and environment, stress, and biological) being considered.

Several theoretical propositions, including notions of growth, decline, and differentiation, suggest that diversity constructs change over the lifespan and/or support lifespan development (Baltes et al., 2006; Sameroff, 2010). As shown in Figure 2, four trajectories are hypothesized. In some domains, normative development is characterized by age-related increases in diversity, as shown by the purple (dot-dash) line in Figure 2. For example, Bronfenbrenner’s (1979, 2006) model of person–process–-context–time systems describes human development as the process of individuals’ ecological environments becoming increasingly differentiated and complex—that is, within-person increases in diversity with age. In other domains, normative development is characterized by age-related decreases in diversity, as depicted by the blue (dashed) trajectory in Figure 2. For example, theories of socioemotional selectivity (Carstensen, 2006) and strength and vulnerability integration (Charles, 2010) suggest that as individuals get older, they increasingly self-select into more positive emotion experiences (and fewer negative emotion experiences), by, for instance, culling their social network to only include close others. One hypothesis driven from these theories is that negative emodiversity, especially across high arousal negative emotions, will, as depicted in the “snapshots” in Figure 2, be high in adolescence and low in older adulthood. Diversity in other domains may follow a U trajectory (dotted green line, Figure 2) or an inverted-U trajectory (solid pink line, Figure 2). For example, the diversity of support types needed from caregivers is often high in infancy and older adulthood and lower in young and middle adulthood, forming a U-shaped trajectory. The differentiation hypothesis of cognitive development (Balinsky, 1941; Garrett, 1946) is an inverted U example, which suggests that the structure of intelligence transforms from a single general ability factor in childhood (low diversity) to more differentiated ability factors in adulthood (high diversity), before dedifferentiating in later adulthood (low diversity). In sum, although various theories suggest that diversity changes over the lifespan, whether diversity is expected to be increasing, decreasing, or remaining stable depends on the domain and life phase.

Interindividual Differences in Diversity

Theoretical propositions of lifespan development also suggest interindividual differences in diversity and/or interindividual differences in intraindividual change in diversity (Baltes et al., 2006). Interindividual differences in emodiversity, for instance, may arise due to context sensitivity. For example, in comparison to nondepressed individuals who tend to experience/express more diverse emotions tied to contextual features (System A: high diversity), depressed individuals tend to engage in response stereotypy by experiencing/expressing the same emotions (System B: low diversity) regardless of context (Rottenberg & Gotlib, 2004; Rottenberg & Hindash, 2015). There may also be interindividual differences in intraindividual change in diversity. For example, although diversity (i.e., inconsistency) in older adults’ cognitive task performance generally declined across 36 weeks as individuals gained experience with the task, diversity of performance declined more steeply for individuals who scored higher on measures of fluid intelligence (Ram, Rabbitt, Stollery, & Nesselroade, 2005). In sum, study of interindividual differences and interindividual differences in intraindividual change makes use of a wide variety of diversity concepts and metrics.

Diversity: Operationalization in Multiple Domains

To illustrate the general utility of diversity-type constructs in the study of lifespan development, we review in more detail how such constructs are being used in the psychological and developmental literatures. Specifically, we examine (a) how diversity-type constructs are being defined and operationalized across a variety of functional domains (socioemotional, self, cognitive, activities and environment, stress, and biological) and (b) how other aspects of function in each domain might be considered with respect to diversity.

Socioemotional Domain

Diversity-type constructs related to emotional functioning include emodiversity, expressive flexibility, regulatory flexibility, and social diversity.


Emodiversity is defined as the extent to which an individual’s emotion-eliciting experiences are dispersed across discrete emotion types (e.g., anger, sadness, joy) (Benson, Ram, Almeida, Zautra, & Ong, 2018; Quoidbach et al., 2014). In some of this work, emodiversity is quantified using the Gini (1912/1955) coefficient,


where cij is the count of individual i’s experiences within j = 1 to m categories (e.g., emotion types) indexed in nondecreasing order (cijcij+1). This ordering means that j also reflects a weighting scheme where the most abundant category is weighted the lowest (j = 1), and the least abundant category is weighted the highest (j = m). Gini-based diversity scores can range from 0 to 1, with higher values indicating more diverse (emotional) ecosystems. The evidence thus far suggests that, among young and middle-age adults, greater diversity in both positive and negative emotions is associated with better mental and physical health, including fewer depressive symptoms (Quoidbach et al., 2014) and lower levels of inflammation (Benson et al., 2017; Ong, Benson, Zautra, & Ram, 2017).

Emodiversity sits within a broader literature encompassing intraindividual variability in discrete emotion and other affect-related constructs such as complexity, poignancy, dialecticism, co-occurrence, differentiation, granularity, flexibility, and fragility. For example, quantifications of emotional (and behavioral) diversity have been used to operationalize and study intraindividual change in “fragmentation” (an indicator of emotion dysregulation) from age 24 to 60 months (Helm, Ram,Cole, & Chow, 2016). Metrics and methods used to quantify these various constructs include the intraindividual standard deviation (iSD), intraindividual covariation, multilevel models, and p-technique factor models. Often, constructs (and their operationalizations) are highly correlated, but opportunities for future research exist to better understand the interrelations and redundancies (both conceptual and mathematical) among the constructs, the circumstances in which they may differentially predict outcomes of interest, and how they can be used to concretely test competing predictions between theoretical perspectives (Hardy & Segerstrom, 2017; Scott, Sliwinski, Mogle, & Almeida, 2014).

Diversity metrics may also be useful in examinations of emotion regulation strategies (e.g., regulatory flexibility, expressive flexibility), such as acceptance, cognitive reappraisal, problem-solving, self-criticism, worry, rumination, suppression and avoidance, and the deployment of these strategies based on contextual features (Aldao, Sheppes, & Gross, 2015; Bonanno & Burton, 2013; Bonanno, Papa,Lalande, Westphal, & Coifman, 2004; Dixon-Gordon, Aldao, & De Los Reyes, 2015; Hollenstein, Lichtwarck-Aschoff, & Potworowski, 2013; Kashdan & Rottenberg, 2010; Westphal, Seivert, & Bonanno, 2010). For example, reappraisal and suppression are strategies that reflect reinterpreting the meaning of an emotion-inducing stimulus and inhibiting the expression of emotions and emotion-related behaviors, respectively (Gross & John, 2003). These emotion-regulation strategies could also be operationalized with respect to the implied diversity in thoughts and or emotions, where cognitive reappraisal would manifest as greater diversity of thought content and emotion experiences, whereas suppression (and relatedly, rumination) would manifest as lower diversity in thought content and emotion experiences.

Social Diversity

Social diversity is defined as the extent of dispersion of social interactions across social partner types (e.g., romantic partner, family member, friend, roommate, work colleague, salesperson) and operationalized using a variant of Shannon’s entropy (Equation 2; Ram, Conroy, Pincus, Hyde, & Molloy, 2012; Vogel, Ram, Conroy, Pincus, & Gerstorf, 2017). Similarly, interpersonal behavior diversity is defined as the dispersion of social interactions across behavior types (e.g., dominant, submissive, agreeable, quarrelsome; Moskowitz & Zuroff, 2004).

Placed in a lifespan developmental framework, the convoy model (Antonucci & Akiyama, 1987), and socioemotional selectivity theory (Carstensen, 2006) suggest that younger adults have more diverse social networks that facilitate their information-seeking goals, whereas older adults have less diverse social networks that maximize their emotionally meaningful connections with close others. Other work has utilized entropy, sample entropy, and Lempel-Ziv complexity as measures of variability in infants’ emotional and behavioral states (Messinger, Ruvolo, Ekas, & Fogel, 2010; Montirosso, Riccardi,Molteni, Borgatti, & Reni, 2010). For example, research on infant–mother dyads’ social communication has found more variability in responses of infants to their mothers in the initial paly and reunion episodes of the face-to-face still-face paradigm (i.e., less diversity) compared to the still-face episode (Montirosso et al., 2010). Possibilities for future research include examinations of concordance and divergence among constructs and empirical research on event-contingent and age-related changes in the full array of socioemotional diversity constructs.

Self Domain

Diversity metrics are also relevant to theories and constructs pertaining to perceptions and behaviors that reflect the self, including self-complexity and personality variability.


Self-complexity refers to the extent of dispersion of traits across self-aspect categories (Linville, 1985; McConnell, 2011). Theory and empirical research indicate that identity formation occurs throughout the lifespan. As early as two years of age, children begin to form representations of a self. With increasing age, they identify with fewer and fewer self-concept domains (Harter, 1998) and show more differentiation in level across domains (Marsh, 1986). Erikson’s (1959) stage theory of development emphasizes that trying out different social roles during adolescence and early adulthood is a key process in identity formation.

One paradigm for measuring self-complexity requires that individuals sort cards with self-relevant adjectives into piles corresponding to distinct aspects of themselves (e.g., family life, work life). The card-sort is then quantified using an “H statistic” that indicates the extent of redundancy in binary data (Attneave, 1959). Specifically, H is a rescaled version of Shannon’s entropy where scores reflect the difference from maximum possible entropy and are calculated as


where m is the number of self-aspect groups an individual created in the sort, and pj is the proportion of adjectives sorted into each group, j, created in the sorting process. A higher H score indicates more independent self-aspects or higher self-complexity whereas a lower score indicates more overlap in self-aspects. Other self-complexity research has proposed alternative metrics such as the number of unique aspects and the average amount of overlap obtained through comparing all possible combinations of groups (Rafaeli-Mor, Gotlib, & Revelle, 1999; Rafaeli-Mor & Steinberg, 2002),


Altogether, these measures have provided useful ways to extract diversity information from participants and subsequent tests of self-complexity theory.

Personality Variability

Personality variability is defined as dispersion in personality characteristics across occasions and contexts. Prior work characterizing intraindividual variability in personality states has used approaches including calculating iSDs (Fleeson, 2001),


where xit are the repeated measures of relevant behaviors for a particular personality trait (e.g., agreeableness) for person i on t = 1 to T occasions, and x¯i represents the intraindividual mean (iMean) for person i on personality trait x. Higher scores indicate more variability in everyday behaviors reflective of personality traits. Developmental studies have examined differences in behavioral tendencies (iMeans and iSDs) among younger and older adults. For example, Noftle and Fleeson (2010) draw on the GLIDE-STRIDE theory of personality across the lifespan, hypothesizing that efforts toward self-improvement and continuous learning are both associated with behavioral variability throughout adulthood (rather than limited to younger adulthood), through facilitating more flexible and adaptable behaviors based on situational features. Consistent with the theory, the results indicated all age groups showed variability in behaviors (i.e., younger adults, middle-age adults, and older adults), but particular personality constructs were more variable for younger adults (e.g., agreeableness, emotional stability) and others were more variable for older adults (e.g., openness).

Extensions of this work into multivariate, nonparametric space have utilized methods from geography to quantify individuals’ behavioral tendencies as landscapes, noting that intraindividual variability may not manifest as a normal distribution (Ram et al., 2013, 2016). With this approach, interindividual differences in these intraindividual behavioral landscapes are quantified using bivariate nonparametric kernel density estimates and Earth Mover’s Distance. Future research may also consider quantification of the within-person variation in personality-relevant behaviors using a categorical biodiversity metric (e.g., Gini coefficient, Equation 3) and more explicit examination of how personality variability changes within-person across over the lifespan.

Cognitive Domain

Diversity metrics are also used to study aspects of cognitive development, including cognitive differentiation and dedifferentiation, cognitive flexibility, divergent thinking, wisdom, and language complexity.

Cognitive Differentiation and Dedifferentiation

Cognitive differentiation refers to the extent of dispersion of levels (i.e., scores) across cognitive abilities (e.g., Thurstone’s Primary Mental Abilities Test assessing number ability, verbal meaning, word fluency, inductive reasoning, and spatial orientation; Thurstone & Thurstone, 1949). It is typically quantified through sample-level covariances among scores on different cognitive ability tests in studies examining between-person or age-related differences and through use of within-person covariances of scores from different cognitive ability tests in studies examining age-related changes (Hülür, Ram,Willis, Schaie, & Gerstorf, 2015).

Lifespan theories of intellectual development suggest that, in early childhood, children’s intellect is characterized by a single general ability (i.e., individuals who score high on one ability also score high on other abilities). In late childhood and adolescence, this general ability differentiates into different types or more specialized forms of cognitive abilities (i.e., scoring high on one ability does not imply a high score on another ability). This differentiated structure of cognitive abilities remains relatively stable through adulthood and then in older adulthood goes through a dedifferentiation process where the structure of cognitive abilities shrinks back into a single general ability (Balinsky, 1941; Baltes et al., 1980; Burt, 1954; Garrett, 1946).

Related to the extent of cognitive differentiation is the extent of cognitive variability (Hultsch & MacDonald, 2004; Slifkin & Newell, 1998; Stawski, Smith, & MacDonald, 2015), measurement of which has been decomposed by different research groups into (a) diversity, the dispersion of reaction time (RT) scores across individuals and quantified using sample-level standard deviation; (b) dispersion, the dispersion in an individual’s RT scores across tasks and quantified using across-task iSD; and (c) inconsistency, the dispersion in an individual’s RT scores across time and quantified using across-trials iSD. Extensions into a multivariate space include a latent factor representation of inconsistency across a set of letter search task conditions (Ram et al., 2005). Although the nomenclature has become somewhat confusing, work in this area highlights the value of considering diversity in multiple aspects of cognitive function.

Cognitive Flexibility

Cognitive flexibility refers to the extent of dispersion in individuals’ application of knowledge and cognitions across different contexts (Martin & Rubin, 1995; Spiro, Coulson,Feltovich, Anderson, 1988). Spiro and colleagues (1988; see also Spiro, Vispoel,Schmitz, Samarapungavan, & Boerger, 1987) focused on knowledge acquisition and utilization in formal learning settings, typically measured through use of experimental lab studies where individuals learn material through teaching practices promoting either cognitive flexibility or rigid context-specific knowledge. Other researchers conceptualize cognitive flexibility more broadly in terms of decision-making in everyday life (e.g., how to behave, which situations to self-select into) and problem-solving, measured through use of questionnaires (Martin & Rubin, 1994, 1995; Paulhus & Martin, 1988). Vygotsky’s (1978) zone of proximal development also reflects ideas of cognitive flexibility wherein adults provide scaffolding in order to facilitate greater cognitive flexibility in learning that the child would not be able to accomplish on his or her own.

Divergent Thinking

A related construct, divergent thinking (in some cases referred to as flexibility of closure or adaptive flexibility), refers to the extent of dispersion of thought processes and ideas across different problem-solving attempts in the creative process (Guilford, 1956; Thurstone, 1944). Generally, diversity in ideas (i.e., thought trials) is considered necessary for creativity (Dorfman & Gassimova, 2017). Divergent thinking tasks are often figural or verbal. For example, the “Brick Uses” test (also more generally called the Alternative Uses Test) requires that an individual list as many uses for a brick (or other common object) within a designated period of time (Torrance, 1966). Scores are computed to reflect fluency (number of uses), flexibility (number of unique solution categories), and originality (average frequency scores for each solution out of all solutions provided by all participants are calculated, and subsequently average frequency scores are calculated for each participant). Empirical research on adolescents and young adults found consistency across age groups in verbal divergent thinking fluency and flexibility but age-related increases in originality (Kleibeuker, De Dreu, & Crone, 2013). In adulthood, data from cross-sectional, longitudinal, and cross-sequential designs suggests curvilinear age gradients in divergent thinking, with scores increasing until midlife and then decreasing into older adulthood (McCrae, Arenberg, & Costa, 1987).


Wisdom is defined as the extent of dispersion of life experiences across (generating) different types of knowledge (Baltes & Smith, 1990). Knowledge types include rich factual knowledge; rich procedural knowledge; knowledge about the different contexts of life (lifespan contextualism); knowledge about different types of goals, values, and priorities (relativism); and knowledge about the indeterminacy/unpredictability in life (Smith & Baltes, 1990). In this framework, the authors emphasize use of longitudinal designs to measure these criteria in response to life events and suggest that richness (variety) in these domains is acquired over time. Past research on wisdom has primarily utilized sum scores from self-report measures (e.g., Taylor, Bates, & Webster, 2011); however, it seems that biodiversity metrics might be particularly useful for quantifying dispersion of knowledge-generating experiences across knowledge types.

Language Complexity

Language complexity refers to the extent of dispersion of language production or language consumption (e.g., comprehension during reading or listening) experiences across language features (e.g., grammatical forms, vocabulary, sentence structures). Theories of language acquisition suggest that diversity of vocabulary and sentence structure increase rapidly in the first years of life, increase more slowly in adolescence and early adulthood, remain stable in midlife, and then decline in older adulthood (Cheung & Kemper, 1992; Halliday, 1975; Kemper, Kynette,Rash, O’Brien, & Sprott, 1989). A variety of metrics are used to quantify language complexity, including number of words per sentence, number of syntactic clauses per sentence, number of grammatical types, and processing demands of various sentence structures. Cheung and Kemper (1992) calculated linguistic complexity with 11 metrics and examined which metrics were most useful for examining age-group differences among adults between 60 and 90 years old. Results revealed 8 of the 11 metrics showed age-related differences. The authors further suggested that the metrics be combined into three first-order factors indicated by a second-order complexity factor.

In this section, we reviewed a number of cognitive diversity-type constructs, including cognitive differentiation and dedifferentiation/integration, cognitive flexibility, divergent thinking, wisdom, and language complexity. Notably, the study designs primarily make use of laboratory-based experimental tasks. Future research on diversity in the cognitive domain might consider how to measure diversity in daily life—for example through ecological momentary assessment techniques or passive monitoring of digitally mediated behavior (e.g., language complexity of text messages).

Activities and Environment Domain

Diversity metrics are also relevant to theories and constructs pertaining to the activities and environments in which individuals participate and are embedded.

Activity Diversity

Activity diversity is defined as dispersion of experiences across activity types (e.g., volunteering, giving help, spending time with family, physical activity, paid work, chores, leisure) and has been measured using a variety of methods, including Shannon’s entropy (Equation 2; Lee et al., 2018), questionnaire items aggregated into a mean or sum score (Jacobs Bao & Lyubomirsky, 2013; Sheldon & Lyubomirsky, 2012), or counting the number of unique places an individual reported visiting (Quoidbach, Dunn,Hansenne, & Bustin, 2015).

Prior research on activity diversity indicates associations with emotion experiences and variations based on age and time scale considered. For example, one study examined activity diversity across multiple time scales (i.e., day, week, month, and year) and found that higher diversity in activities was associated with higher happiness, except when relatively short time scales were considered (Etkin & Mogilner, 2014). Activity diversity is also implied in the Hedonic Adaptation Prevention model, suggesting that one way to avoid the hedonic treadmill—the relatively short-lived boost in positive affect in response to a positive event—is to seek varied positive events/activities (Jacobs Bao & Lyubomirsky, 2013; Quoidbach et al., 2015; Sheldon & Lyubomirsky, 2012). Finally, from a lifespan developmental perspective, research has shown that individuals who increased in their activity diversity over a 10-year period also increased in their positive affect. However, greater diversity in activities was associated with higher well-being for older adults, whereas greater activity diversity was associated with lower well-being for younger adults (Lee et al., 2018).

Environmental Complexity

Environmental complexity is the extent of dispersion in the environment’s features (e.g., physical, social, symbolic) across settings (e.g., home, school, work, neighborhood, city, state, country). For example, Bronfenbrenner’s (1979, 2006) model of person–process–context–time systems and ecological theory suggests that individuals’ environments become increasingly differentiated and complex—within-person increases in diversity—with age. In research on participation in school and work context, studies indicate that individuals’ initiative, thought, and independent judgment were related to more complex work and, in turn, that work environments with more complexity were associated with higher intellectual functioning (Kohn & Schooler, 1973; Miller, Kohn, & Schooler, 1985).

Environmental complexity can also be examined through assessment of physical features of the environment. Cassarino and Setti (2016) put forth a framework of environmental complexity spanning micro (e.g., color, spatial properties, clutter), meso (e.g., legibility of street signs, visual richness), and macro (e.g., nature, urbanization) scales. Environmental complexity at the different scales can then be examined in relation to individual and/or age-related differences in cognitive performance, environmental preferences, and perceived usability. Passive data monitoring techniques such as GPS coordinates, automatic activity detection algorithms (e.g., classifying movement as walking or running), and possibilities to connect individual-level data to geospatial databases (e.g., county-level health indicators, neighborhood resources such as parks, population density) all provide exciting future directions for examining interplay between environmental diversity and lifespan development.

Stress Domain

Diversity metrics are also relevant to theories and constructs pertaining to stress.

Stressor Diversity

Stressor diversity reflects the extent of dispersion of stress-inducing experiences across stressor types (e.g., interpersonal, work/education, home, discrimination) and has been measured using Shannon’s entropy (Equation 2). Applying ideas from Hobfoll’s (1989) Conservation of Resources model, Koffer, Ram, Conroy, Pincus, and Almeida (2016) suggest that higher stressor diversity is better for well-being because dispersion across stressor types ensures that no one stressor can deplete all of a person’s resources (e.g., social capital). In line with their hypotheses, initial results indicate that high exposure to a smaller set of (recurrent or “chronic”) stressors is indeed worse for well-being than high exposure to a diverse set of stressors.


Another important diversity-type construct used in stress research is allostasis—the extent of dispersion of physiological responses across systems (e.g., autonomic nervous system, hypothalamic–pituitary–adrenal axis, cardiovascular system, metabolic system, immune system)—more of which allows an individual to adapt to features of the environment. Whereas the immediate responses to features of the environment is termed allostasis, prolonged over- or underactivity of any one or combination of systems is referred to as allostatic load (McEwen, 1998). Allostatic load is quantified in a variety of ways, including counting which of a specified number of biomarkers (e.g., 10) indicate risk,


where cij reflects whether individual i for biomarker j was above (= 1) or below (= 0) a biomarker-specific “high-risk” threshold (scores for each person ranging from zero to 10; Seeman, Singer, Rowe, Horwitz, & McEwen, 1997; see Juster, McEwen, & Lupien,2010, for a review of the various allostatic load metrics). Future research on diversity in the stress domain might include separation of stressors into different functional types (e.g., positive stressors such as birth of a child and negative stressors such as loss of a job; controllable vs. uncontrollable stressors) and increased precision in defining and measuring whether a stressor or physiological response is chronic or acute.

Biological Domain

Diversity metrics are also relevant to theories and constructs pertaining to biological functioning such as mobility diversity, microbial diversity, and dietary diversity.

Mobility Diversity

One form of mobility diversity is movement complexity, defined as the extent of dispersion of movements across movement forms (e.g., throwing, finger tapping, walking, running, and activities of daily living such as bathing, dressing, self-feeding) or across different environments. Movement complexity is studied observationally and through experimental manipulations, with measurements pertaining to absence or presence of various movement forms (Bayley, 1969). Gesell and Ames (1940) describe the ontogenetic flows of increasing complexities in movement (e.g., different arm positions) during infant development. Similarly, McGraw (1945) described development of movement complexity as progression through seven stages of erect locomotion. For example, infants develop the capacity to extend their arms forward, to flex their arms inward, and eventually to form right angles—first supporting their body with the forearms and eventually supporting their body with their hands. In laboratory designs, the complexity of movement is studied through experimental manipulations such as submerging an infant torso-deep in water or varying treadmill speed, sometimes to different speeds for each leg (Thelen, Ulrich, & Wolff, 1991). Studies of aging have also used questionnaires to assess activities of daily living, and movement-related behavioral flexibility in response to environmental and personal challenges reflect processes of decreasing diversity in movement forms (Katz, 1983; Penger & Oswald, 2017; Spector, Katz, Murphy, & Fulton, 1987).

Another form of mobility diversity, motor performance variability (also referred to as intraindividual sensorimotor variability) is defined as the extent of dispersion of performance levels across situations/events/trials (e.g., throwing darts, kicking a soccer ball, trials, testing sessions). Although lower variability in performance is typically considered to be an indicator of better performance/success (Slifkin & Newell, 1998), variability in performance is also considered as a natural occurrence in a dynamic system (Kelso, 1997). Data are typically collected during movement and posture tasks (running on a treadmill, drawing, finger tapping, etc., similar to the movement complexity literature) and quantifying extent of variability using metrics such as the iSD, where a higher score indicates greater magnitude in performance deviations from a person’s average level of performance or the time-dependent and frequency-dependent properties of the movements using approximate entropy and spectral analysis. For example, Vaillancourt and Newell (2000) differentiated performance between low-level Parkinson’s disease and age-matched control groups’ performance on a finger postural tremor task using approximate entropy and spectral analysis methods—suggesting that these measures of diversity might be used as early markers of age-related or disease-related change.

Microbial Diversity

Diversity-type thinking has been applied to other aspects of human health. Recently, there has been substantial study of microbial diversity in the human intestine—the extent of dispersion of microbial cells across types of Bacteria and Archaea species. DNA embedded in fecal samples are sequenced and classified by type—distinct operational taxonomic units (OTU; also called ribotypes) that share at least 98% similarity. Dispersion across types is then quantified in terms of both total number of OTUs represented and relative abundance across types and examined with respect to interindividual differences and/or intraindividual change. For example, certain diseases such as Crohn’s disease are marked by less diversity in fecal microbiota (Manichanh et al., 2006). Humans are born germ free but accumulate up to 100 trillion microbes. The accumulation of such diversity happens quickly in infancy and then decreases with age (Ley, Peterson, & Gordon, 2006). However, even in adulthood, higher microbial diversity is seen as beneficial. Diversity of what we put into and what lives inside our stomachs influence health and development.

Dietary Diversity

Dietary diversity is defined as the extent of dispersion in food items and nutrients within (or across) food group(s). It is often measured using the Healthy Eating Index, a questionnaire assessing the types and quantities of food an individual consumes (Guenther et al., 2013; Kennedy, Ohls, Carlson, & Fleming, 1995). Another possibility is to use a biodiversity metric such as Shannon’s entropy (Equation 2; Remans, Wood, Saha, Anderman, & DeFries, 2014). Research examining variety of foods included in a meal and satiety found that humans were more likely to overeat when there was more variety in the food choices presented in a meal (Rolls et al., 1981). Diversity in low-energy density foods such as vegetables is associated with good intake of nutrients, whereas diversity in high-energy density foods such as sweets and fried foods is associated poorer intake of nutrients (Kant, Block, Schatzkin, Ziegler, & Nestle, 1991; McCrory et al., 1999). Given the extensive impacts of obesity on human life, nutrition diversity has important implications for individuals’ development across the entire lifespan (Nicklas, Baranowski, Cullen, & Berenson, 2001). As more data stream in about what individuals are eating (e.g., nutrition tracking) and how they are moving (activity tracking) from smartphones and other passive sensors, we will continue to learn how diversity changes and influences functional change throughout development.

Conclusions From Across-Domain Review

In the previous sections we reviewed how diversity-type constructs are being used to study human behavior and development in socioemotional, self, cognitive, activities and environment, stress, and biological domains. From this review, we identified a series of cross-cutting themes relevant for how diversity is conceptualized and studied: (a) diversity provides a holistic representation of human systems, (b) diversity differs in direction and interpretation, (c) diversity exists at multiple scales, (d) diversity is context-specific, and (e) diversity is flexible to many study designs and data types.

Diversity as a Holistic Representation

Rather than breaking the system into subcomponents (e.g., discrete emotions of anger, fear, joy) and studying subcomponents separately, diversity-type constructs provide for holistic representation of a system (e.g., emodiversity of an emotional ecosystem). Bronfenbrenner’s (1979, 2006) bioecological model explicitly considers human development holistically in terms of dispersion of experiences across different environmental contexts. Thus rather than examining an individual within their family unit—the focus is on how functioning within the family system exists within a larger holistic representation of an individual’s ecological space, including the school, work, neighborhood, and cultural contexts. Conceptualized specifically in terms of dispersion across the entire space, diversity constructs embody the notion that a human is greater than the sum of his or her individual parts (Ford & Lerner, 1992; Kitchener, 1982; Magnusson & Stattin, 2006).

Diversity Differs in Direction and Interpretation

Diversity-type constructs differ in direction (increasing vs. decreasing), interpretation (adaptive vs. maladaptive), and linkages to other constructs, such as health (Li, Huxhold, & Schmiedek, 2004; MacDonald & Stawski, 2015). For example, some constructs may indicate positive adaptation and show age-related increases across the lifespan (e.g., divergent thinking, wisdom), whereas other constructs may indicate positive adaptation and show age-related decreases (e.g., social diversity). Similarly, constructs indicative of vulnerability or poorer functioning may show age-related increases (e.g., cognitive inconsistency) or age-related decreases (e.g., mobility diversity). Complicating matters, some constructs may show U-shaped or inverted U-shaped trajectories across the lifespan (i.e., mobility diversity may actually be better characterized as an inverted U shape). Li and colleagues (2004) suggest that diversity constructs related to use of multiple strategies are more likely to indicate adaptive functioning, whereas constructs reflecting inconsistency in performance may indicate maladaptive or diminished functioning. For example, higher heart rate variability is typically an indicator of physiological health whereas higher allostatic load is typically an indicator of physiological dysregulation. Further work and precision in reporting is needed to identify and understand the modulating factors and underlying mechanisms that link the various types of diversity to individuals’ developmental trajectories.

Diversity Exists at Multiple Scales

Diversity exists at multiple scales, including across temporal scales (e.g., seconds, minutes, hours, days, weeks, years, decades) across spatial scales (e.g., meters, kilometers), and across groupings (e.g., individual, family, city, state, country). While some constructs may have a single time scale that is most useful—for example, cognitive variability represented by fast time scales such as trial to trial—other constructs might have distinct features represented by different time scales (MacDonald & Stawski, 2015). For example, trait personality diversity exists as an individual’s general perception of their personality traits whereas daily personality diversity exists as a quantification of day-to-day differences in behavior. Pushes in developmental science (e.g., Cole, 2016; Gerstorf, Hoppmann, & Ram, 2014) to better understand and clearly articulate the relevant time scales for biopsychosociocultural phenomena, associations among them, as well as the mechanisms, are similarly relevant for diversity-type constructs. As the range of diversity-type constructs expands, clear articulation of the scale(s) in which diversity is calculated is increasingly important. The literature will benefit from more precise labeling of the temporal and spatial scaled covered by any particular diversity-type construct.

Diversity Is Context Specific

Diversity-type constructs in lifespan development research are context dependent. For example, in studies of development of movement complexity in infancy, the range of observed movement patterns greatly depends on physical context (e.g., supporting self, supported by caregiver, on a treadmill, or with legs submerged in water; Adolph & Berger, 2006; Thelen & Smith, 1996). Similarly, in studies of mobility diversity of later adulthood, the range of possible movement patterns depends on environmental features such as traffic, sidewalks, stairs, uneven surfaces, proximity of destinations of interest, and pleasant scenery (Kerr, Rosenberg, & Frank, 2012).

When defining context, many developmental scientists utilize Cattell’s (1952) data box—with the three dimensions of the box organizing where an individual (a data point) exists within time (occasions), within space (variables), and among persons. Conceptualizations of diversity include diversity across categories (variables), time (occasions), and groups (persons). No matter which slices of the data box are used to quantify diversity—and to examine interindividual differences or intraindividual changes in diversity—care should be taken in consider the measurement context. Particularly important conceptually is whether diversity is measured in a context where all types of relevant behaviors can be observed or expressed (e.g., in a laboratory setting) or where behavioral affordances and constraints may differ dramatically across persons or occasions (e.g., naturally occurring contexts).

Diversity Is Flexible to Many Study Designs and Data Types

Our review of diversity-type constructs suggests that the study designs and data types used to quantify diversity constructs are themselves diverse. Data from ecological momentary assessment, daily diary, lab-based observational and experimental tasks, and questionnaires are all facilitating study of diversity-type constructs. As well, observational data obtained in various settings (e.g., families, schools, workplaces), non-lab-based experiments and alongside real-world quasi-experiments are also facilitating operationalization of diversity-type constructs. Diversity is also sometimes measured directly using questionnaire items such as “How much new variety has this change brought to your life?”). Indeed, it is exciting that so many different types of data are informing knowledge of different types of diversity. In sum, the existing literature on domain-specific diversity-type constructs highlights conceptualization of diversity as a holistic representation of human functioning and behavior across the lifespan, consideration of diversity across multiple spatial and temporal scales, examination of how contextual features afford or constrain diversity, and the use of many different types of study designs and measurements to operationalize diversity.

Diversity and Developmental Data: Methodological Considerations

Noting the diversity in the ways diversity is operationalized across domains and studies, this section highlights some methodological considerations pertaining to choice of metric.

Choosing a Metric

Similar to the diverse range of phenomena that can be studied using diversity-type constructs, there are also a variety of metrics that can be used to calculate diversity. Choice of metric can be driven by theory, data, or the interplay between theory and data.

Theory-Driven Approach

Theoretical considerations surrounding choice of metric pertain to whether the diversity-type construct should be operationalized as evenness (the distribution of organisms across species types, e.g., Gini coefficient), richness (the total number of species types, e.g., richness diversity), or both evenness and richness (variety and relative abundance of species, e.g., Shannon’s entropy, Simpson’s index). For repeated measures data, theoretical considerations might also include whether the diversity-type construct of interest is a time-independent dynamic characteristic or a time-ordered dynamic process (Ram & Gerstorf, 2009). Many metrics (e.g., Shannon’s entropy, Simpson’s index, the Gini coefficient, and richness diversity) invoke diversity as a dynamic characteristic, where all the observations are assumed to be independent and identically distributed. These metrics are useful for research questions pertaining to differences in abundances across types, range of experiences across types, and combinations of the two. Research questions interested in underlying processes of human development may instead select methods that consider time-dependent and frequency-dependent properties of a time series (e.g., approximate entropy, spectral analysis). Care should be taken that the metric matches the theoretical concept of diversity.

Data-Driven Approach

In cases where the theoretical conception of the diversity-type construct is less clear and/or is being explored, choice of metric may be more data driven—calculating multiple metrics and comparing. For example, multiple metrics can be examined with respect to scale, distributional properties, and associations with other criterion (or each other). Scaling considerations such as whether the metric is bounded between 0 and 1 have implications for comparisons across samples. Distributional properties of scores are relevant for subsequent statistical modeling, with metrics yielding more normally distributed scores favored in models using diversity as an outcome variable. In some data, the different diversity metrics are highly correlated, and thus it does not matter too much which metric is used (e.g., Benson et al., 2017). In other cases, however, different substantive conclusions are obtained when looking at different metrics—a concern when trying to evaluate generalizability across studies (Budescu & Budescu, 2012). A persistent consideration when working with measures of dispersion and diversity (i.e., measures of variance) is relation with measures of central tendency (e.g., mean). In some cases, diversity scores are highly correlated with mean-level scores (e.g., Benson et al., 2017) and may or may not contain additional information. Choosing diversity metrics that are distinguishable from other variables ensures that diversity constructs are not just “re-mixes” of other constructs. Another consideration involves precision of measurement. Considering richness in isolation is often not preferred in ecology because species cannot be recognized with total precision (Swingland, 2013).

As the diversity literatures in specific domains grow, integrative reviews also become possible and provide further guidance on metric selection. For example, in the biomedical domain, Bravi, Longtin, and Seely (2011) reviewed over 70 variability metrics for summarizing physiological time-series data, resulting in a classification system composed of various domains of variability. Other domain-specific reviews include biomarker complexity and emotional disorders (de la Torre-Luque, Bornas, Balle, & Fiol-Veny, 2016), language complexity (Cheung & Kemper, 1992), environmental complexity (Betak, 1974; Cannon & St. John, 2007; Cassarino & Setti, 2016; Gunawardena, Kubota, & Fukahori, 2015), allostatic load (Juster et al., 2010), and emodiversity (Benson et al., 2017). With many diversity metrics to choose from, and in some cases important nuances among metrics, it is important for researchers to clearly articulate why a particular metric was chosen, the elements (e.g., species) of interest, and the scales (temporal and spatial) of interest. See a tutorial on calculating diversity-type constructs using emodiversity as the focal example.

Future Directions for Using Biodiversity Metrics in Lifespan Development Research

Biodiversity theories borrowed from ecology have provided a foundation for operationalizing and hypothesizing about how diversity-type constructs change over age and relate to other constructs. As this literature grows, we see four theoretical/methodological considerations that may be particularly fruitful: super-diversity, measurement in time and space, identification of “keystone” species, and nonlinearity.


Earlier it was highlighted how diversity constructs capture ideas about holism. For example, social diversity considers how all social experiences, which are dispersed across unique social relationship types, together form an individual’s social ecology. The holistic representation considers social experiences at work with colleagues and social experiences at home with family. Of course, holism too can be conceived at different levels of analysis. One future direction is to consider across-domain diversity and the dynamic interplay among domains—sometimes termed super-diversity (Vertovec, 2007; also see special issue in Ethnic and Racial Studies, 2015). For example, in describing the nature and dynamics of communities in the United Kingdom, researchers are moving beyond typical conceptualizations of diversity as pertaining to ethnicity and suggesting that diversity should also consider other types of diversity, such as differences in immigration status, divergent labor market experiences, gender and age profiles, spatial distributions, and so on. Conceptually the diversity of diversities concept is clear. However, further elaboration is needed on how to quantify super-diversity. One possibility is to use variable-centered approaches and identify the factor structure of multiple diversity-type constructs (e.g., are those who are high in emodiversity also high in environmental diversity or stressor diversity?). For example, in ecology, a review of the research on the “habitat heterogeneity hypothesis” found a general positive association between habitat heterogeneity and species diversity but also noted nuance pertaining to specific types of species (i.e., moderators of this association; Tews et al., 2004). Another possibility is to use a person-centered approach and identify groups of individuals who share similar diversity profiles. Extending ideas of holism into cross-domain quantifications of super-diversity allows for coalescence across individuals’ behaviors, thoughts, emotions, and experiences as well as the contexts in which they are embedded—all aspects that contribute to individuals’ development.

Measuring Diversity in Time and Space (Context)

Measuring diversity in time and space provides opportunities to examine diversity as an alternative index to chronological age, to identify the processes generating diversity, and to study how diversity is influenced by “disturbances” or events in one’s environment.

Diversity as an Alternative Index to Chronological Age

In lifespan developmental research, chronological age is often used as a proxy for the underlying biological, physiological, or psychological processes that drive intraindividual change (Birren, 1959; Birren & Cunningham, 1985). Various types of psychological, social, behavioral, and biological diversity might be considered as alternatives indices of development. For example, infants with lower diversity in behavioral responses in the face-to-face still-face paradigm may be more developmentally advanced than those with higher diversity. Similarly, wisdom, operationalized as higher diversity in an individual’s scope of cognitions, may be an index of developmental maturity. In each domain it may be possible to identify which constructs show cumulative diversity over time and thus provide for more precise identification of where an individual is situated within his or her lifespan.

Processes Generating Diversity

Developmental science is characterized by study of change and study of the processes that lead to specific outcomes (Baltes, Reese, & Nesselroade, 1988). Prior work on diversity constructs has mostly described how diversity in one or more domains/behaviors is related to differences or changes in other aspects of behavior or function. Emphasis on testing such general patterns is also common in the ecology literature. However, more research is needed to understand and explain the relations among different kinds of diversity (Hooper et al., 2005). For example, one could consider what social interaction processes (e.g., synchrony, coregulation, contagion, reciprocity, reactivity, reinforcement) specifically produce interindividual differences or intraindividual changes in social diversity. For example, social reinforcement is a generating process implicated in language complexity. Specifically, repetition of words from caregivers allows infants to learn the statistical regularities among syllables in a fluent speech stream, which facilitates learning of word boundaries (Saffran, Aslin, & Newport, 1996). In the adult literature, age-related differences and/or changes in emotion regulation processes likely inform the generation of emodiversity. For example, the selection, optimization, and compensation with emotion-regulation frameworks suggest age-related changes in resources and motivation are accompanied by changes in emotion-regulation strategy use, with younger adults more likely to utilize reappraisal to modify emotional responses to a situation (i.e., generating higher emodiversity over time) and older adults more likely to utilize situation selection to avoid high arousal negative emotion experiences (i.e., generating lower emodiversity over time; Urry & Gross, 2010). Better understanding of the processes generating diversity in various domains is necessary for understanding the mechanisms that facilitate diversity and for research aimed at modifying diversity-type constructs.


In the ecology literature, researchers are interested in how patterns of diversity change over time (Swingland, 2013), including how perturbation(s) to the (eco)system—disturbances that occur with specified duration, frequency, intensity and scale—may either destroy or enhance biodiversity (Davis & Moritz, 2013; Keane, 2013; Magurran & McGill, 2011). Recall that one of the key biodiversity hypotheses pertains to higher diversity permitting ecosystem resilience in response to a perturbation. Thus one opportunity for future research in domains relevant to lifespan development is to examine whether individuals with higher diversity maintain functioning or “bounce back” more quickly from perturbations (e.g., major life events). Researchers might also study the impact of different types of disturbances. Some disturbances may have a more intense impact than others, and some disturbances may have a relatively specific impact whereas others may have a wider ranging impact.

Disturbances are considered as a form of environmental variability and are typically discrete events in time. There are several ways to study the impact of a disturbance on an ecosystem including examining changes in diversity between pre-disturbance and post-disturbance assessments, examining how long it takes for the ecosystem’s biodiversity to recover after the disturbance, and examining cumulative effects of disturbance events (Davis & Moritz, 2013; Keane, 2013). For example, periodic forest fires are considered beneficial disturbances for reasons including clearing out disease-carrying insects and removing debris on the forest floor that could contribute to larger hotter fires that would damage soil and increase erosion (for review, see DeBano, Neary, & Ffolliott, 1998). Other ways of quantifying and describing disturbances include timing, extent of influence, and magnitude (Davis & Moritz, 2013). Given the possibilities, it is important to explicitly detail the event being considered the disturbance and also to clearly operationalize the features of the disturbance under study. Keane (2013) provides a useful overview of several disturbance characteristics and corresponding descriptions (Table 1, p. 569).

Many such opportunities also exist to examine disturbances over the lifespan. A disturbance in early childhood might include starting school. In young adulthood, moving out of one’s childhood home to attend college is a disturbance that may influence diversity in a number of domains such as social contacts, identity, and cognition and may also represent a change in the rate of change in diversity given that both the school environment and home environment changed in this transition. In adulthood, events such as marriage or the birth of a child and functional/health disturbances such as breaking a hip may have implications for diversity-type constructs. Disturbance also relates to everyday occurrences such as individuals’ cognitive, emotional and behavioral reactions in various situations—sometimes termed turbulence or relational turbulence in dyadic situations (Solomon & Knobloch, 2004). Assuming that some individuals may respond differently to a similar disturbance, there are possibilities for examining interindividual differences in diversity change as the result of a disturbance.

Keystone Species

In some ecosystems, all species may be considered equally important to the functioning of the system, whereas in other ecosystems, there may be certain species—keystone species—that may have a disproportionately large effect on the system, even though they might have relatively low abundance (Paine, 1969; see Davic [2003] for a summary of the debate in the ecology literature regarding keystone species). In ecology, the general hypothesis is that diversity is good for ecosystem health because if one species is removed, others that can perform similar functions can take its place. However, research on the health of coral reefs shows that some of the key jobs for maintaining the reefs are done by only a single species (Mouillot et al., 2014). Thus, if that species is lost, no other species can perform similar functions and the entire reef suffers. Even with high biodiversity, the keystone species is essential. In the human domain, research on the microbiome has for example identified specific bacteria types (Bacteroides fragilis and Bacteroided stercosis) that, despite having only moderate abundance, play a keystone role by interacting with over twice as many other types of bacteria compared to the rest of the bacteria types (Fisher & Mehta, 2014). These bacteria play a key role in how the entire system functions. Other domains may also have keystone behaviors. For example, the emotional ecosystem may depend heavily on presence of calm. If other emotions are removed (e.g., anger, excited), the system will function fine, but if calm is removed the whole system may become unstable and fail.

While the conceptual identification of keystone species is possible, it is not yet clear how to represent a keystone species mathematically in the calculation of diversity metrics or empirically. One possibility is through weighting within the diversity metric. For example, Equation 1 can be expanded to include a weighting scheme,


where each category or species is weighted by its relative value to the system, rj. Keystone species would have higher weights, whereas replaceable or overlapping species would have lower weights. A second possibility, applied in research on food webs, is to use a network approach where keystone species are identified through indices such as node degree, keystone, centrality, and key-player approaches (Jordán, Liu, & Davis, 2006). The key-player approach examines which nodes, if deleted, maximally disconnect the network and which nodes spread information in the fastest way to other nodes (Borgatti, 2003). A third possibility is to adopt an experimental or quasi-experimental approach where species are added and removed to assess for dramatic changes in diversity. In sum, there are a number of opportunities to be explored in future research seeking to develop the theory about which domains might be affected by keystone species, what the keystone species are in a domain, and the methods for mathematically or experimentally examining (and potentially modifying) the influence of a keystone species.


Opportunities exist for future research pertaining to nonlinear and nonparametric analysis methods for diversity-type constructs. Thus far, interindividual differences in diversity-type constructs have been analyzed within linear regression-based methods. However, Benson and colleagues (2017), among others, have demonstrated that there are clear ceiling and floor effects in the quantification of diversity. In addition, suppression effects have been noted when examining both diversity and mean levels in the same model, which is likely how this nonlinearity is manifesting itself. While there are valid theoretical arguments to account for this suppression (e.g., Quoidbach et al., 2018), another possibility is to move beyond the linearity assumptions and use methods that are better suited for and can accommodate constraints imposed in the measurement process (i.e., the calculation of diversity-type constructs). Finally, it may be that experiences (such as emotion experiences) are driven by nonlinear processes—so even the initial responses might be mischaracterized by the measurement process wherein the daily experience scores are put on a linear scale. This suggests future work into measurement of constructs yielding the data used to calculate diversity-type constructs.

Another nonlinearity consideration pertains to whether diversity is linearly associated with outcomes of interest or whether there are boundaries at the lower and upper ends with adaptiveness situated at mid-levels of diversity—termed by some as optimum variability (Guastello, 2015; Schuldberg, 2015). For example, Schwartz’s (2004) paradox of choice suggests that individuals enjoy exercising their freedom of autonomy and making choices, but at a certain level of choice diversity, the association between choice and well-being switches to be in the negative direction. This type of example implies an inverted-U type association. Examples of this are also seen in infant motor development, where infants stepping increases in the first month, then decreases when a different system comes online, and then increases again (Thelen & Smith, 1996). Another possibility is that once a given level of diversity is reached, there are no longer added benefits (or losses) from becoming more diverse. For example, research on the Three Good Things exercise used in positive psychology shows that once an individual describes three unique features of his or her day, any additional “good things” described are no longer associated with upticks in well-being (Peterson, 2006). This type of association implies an exponential decay increasing form, where there is quick initial increasing growth, which then levels off at some limiting value.

Rooted in biodiversity theory pertaining to adaptive functions, responses to perturbations, and ecosystem resilience, this article has highlighted several avenues of future research: examining the utility of biodiversity metrics as an alternative index to chronological age, identifying the processes generating diversity, studying the effects of disturbances on the system, demonstrating the existence and influence of keystone species, and elaborating the nonlinearity surrounding diversity-type constructs and their relations with other interindividual differences and intraindividual changes.


The introduction of diversity theories and metrics into lifespan developmental research aligns with the increasingly recognized view that systems, regardless of domain or discipline, share similar properties (Boulding, 1956). The theoretical and methodological approaches used in study of biodiversity open exciting possibilities that can advance the study of lifespan development—especially when operational definitions, measurement techniques (including diversity metric, contextual features, and temporal and spatial scales), and hypotheses are explicit and specific. Relevance is clear across a broad range of psychological, social, behavioral, physical and environmental phenomena, study designs, and data. As we learn more and push the boundaries of possibility, it will be exciting to see new knowledge relevant to lifespan development and healthy aging emerge.


This work was supported by the Pennsylvania State University Graduate Fellowship, the National Institutes of Health (R01 HD076994, R24 HD041025, UL TR000127), and the Penn State Social Science Research Institute.


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