Each year, we hear that people are gaining weight and that chronic health problems like obesity, heart problems, and diabetes are on the rise.  It’s commonplace to ascribe these trends to personal lifestyle choices, such as the lack of exercise and diet, as well as the increasingly pervasive nature of fast food and processed, high-sugar foods.

However, there may be additional risk factors that are harder to control, such as genetics, and—as  a provocative new article in PLoS One (open access) suggests—birth order.  Specifically, first-born children might be more prone to these kinds of chronic health issues later in life:

Recent work has suggested that birth order may be a non-modifiable risk factor for obesity. Current evidence suggests that first-born infants grow faster than later-born infants. Dunger et al. suggest that the in-utero growth of first-born babies may be restrained as they have lower birth weight and accelerated post-natal catch-up growth, both of which are risk factors for obesity and cardiovascular and metabolic diseases, in adult life. However, whether first-born individuals have elevated metabolic risk in adulthood remains unknown. A recent study found that first-borns had a 4-fold risk of increased fat mass in early adulthood compared to later-borns. Neither of these studies evaluated the magnitude of metabolic risk induced by such greater weight and adiposity.

…Here we investigate the associations of birth-order with metabolic phenotype in early adulthood using data from a birth cohort of Brazilian young men. We tested two hypotheses. First, we wanted to confirm that first-born status was associated with low birth weight and faster infant growth. Second, we tested the hypothesis that metabolic risk was increased in first-borns compared to later-borns.

What did they find? What implications might their work have for public health given the kinds of global population changes we expect over coming decades?

Some results (excerpts):

• After adjusting for family income, maternal education, household assets score and maternal smoking in pregnancy, first-borns had significantly lower mean birth weight.
• First-borns also showed faster weight gains during infancy and had greater mean height and weight at 43 months.
• This greater weight and height tracked into early adulthood, with first-borns being significantly taller and heavier than later-borns.
• Total cholesterol and low-density lipoproteins were higher among first-borns.
• Our analysis suggests that low birth weight does not itself explain the increased metabolic risk associated with birth order. Rather, rapid post-natal weight gain appears most important, although such rapid growth is itself a response to low birth weight. Broadly similar growth patterns have been linked to the occurrence of type 2 diabetes and coronary events in adults.

So why do these patterns happen?  Here is their hypothesis:

The lower birth weight of first-borns can be attributed to materno-fetal physiological interactions. Following implantation, cells from the outer layer of the blastocyst, known as trophoblast, invade the maternal endometrium and alter the structure of the arteries that transfer blood to the placenta. Such modification decreases maternal resistance and increases placental blood flow. These changes then impact on the placental dynamics of subsequent pregnancies, such that second-born neonates are well known to have higher average birth weight than first-borns. Dunger et al. suggested that first-born children have higher glucose levels compared to later-borns, an effect most likely due to the combined effect of insulin resistance due to the increased adiposity and to the possible in utero programming of the insulin glucose axis. Thus, the increased adult body weight and adiposity of first-borns is likely to be induced at least in part by the maternal constraint of intra-uterine growth. However, other mechanisms may also be important. There is preliminary evidence in animals and in humans, that the novel experience of the first pregnancy could raise the level of apprehension in primigravid women, thereby potentially affecting the growth of the foetus via modulation of the vascular and endocrine functions of the feto-placental unit. Maternal emotional stress is an established risk factor for low birth weight, intrauterine growth retardation, preterm delivery and still-birth. Specifically, circadian cortisol secretion pattern appears to be distinctive in primiparous women and an alteration of the hypothalamus-pituitary axis (HPA) function could modify maternal glucocorticoids levels and affect foetal development. Possible mechanisms for birth-order effects on foetal growth merit further research.

And what potential implications might this have for the health of the global human population as we approach 9 billion people on the planet by 2050 and move through demographic transitions, such as reduced family sizes (emphasis mine)?

Our findings contribute to understanding of the early origins of adult disease. Our data show that a demographic factor relevant to all human populations can generate variability in both early growth and later metabolic risk. These findings also have important implications for understanding the increasing prevalence of the metabolic syndrome worldwide, where many populations are undergoing demographic change in response to economic development. Globally, there is a trend towards lower fertility rate, such that increasing proportion of individuals will be first-borns. In Brazil, for example, the average number of children per women (total fertility rate) dropped from 6.0 in 1960 to 1.8 currently.

They conclude with several important qualifications:

[A] number of questions still merit attention. For example, studies should describe in more detail the growth patterns that appear to lead to elevate metabolic risk, and identify the optimal time periods for intervention. Studies should also clarify the relative contribution of different possible underlying mechanisms (growth patterns, psychological factors) to the effects that we observed in these samples. Third, more research is required to establish the magnitude of the effect, whether it is similar in men and women, and whether it amplifies with age, as adverse metabolic profile consolidates. In these samples of young adults, the magnitude of the effect was relatively small, but degenerative diseases are expressed primarily from middle age and early-life effects tend to become more important through adulthood.

Siervo, M., Horta, B., Stephan, B., Victora, C., & Wells, J. (2010). First-Borns Carry a Higher Metabolic Risk in Early Adulthood: Evidence from a Prospective Cohort Study PLoS ONE, 5 (11) DOI: 10.1371/journal.pone.0013907

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Right up there with climate change, biodiversity conservation is one of the most challenging issues at the intersection of nature and culture.  Part of this challenge arises because of genuine differences in how people value other species.

In an interesting forthcoming article in Conservation Biology, Chris Sandbrook and colleagues at Cambridge University argue that these value differences not only show up in society at large, but among conservation professionals, who—like climate scientists—are drawn to the possibility of developing scientific consensuses to inform policy debates:

Conservation biology has been called a crisis science and a mission-driven discipline. Both the mission, and its urgency, seem clear, and there has been a substantial increase in activities intended to address the rapid decline in the variety of life on Earth at all levels of biological organization (structure, composition, and function). Nevertheless, there are tensions within the field about the values that underpin the conservation mission, particularly concerning the nature and singularity of these values and the role of values when conservation professionals try to inform or influence policy.

Recently, the values held by conservation professionals themselves have been debated. Conservation professionals often refer to both instrumental values (the usefulness of nature for humans) and noninstrumental or intrinsic values, and there may be an element of opportunism when they do so. Thus, although some may privately base the positions they hold on intrinsic values, they may espouse use-value arguments in public, adapting arguments to the interests of their audience. Some call for conservation scientists to return to a conservation ethic derived from intrinsic values

…[Others] propose a more pragmatic engagement with material values of nature in their focus on what they see as the “hard socioeconomic realities in real-world conservation problems.” The environmental philosophy of pragmatism, with its acceptance of both intrinsic and instrumental values of nature, is the hallmark of adaptive management

To study values held by conservationists, the research team posed a set of values to scientists and asked them to rank the degree to which they agreed or disagreed with the statements (Q methodology).  The responses were then run through a set of statistics (factor analysis) to distill the huge pile of value-by-person data into four overarching factors that summarized the main values held.

Their results suggest that consensus building may not only be difficult, it may be counterproductive…

Excerpts edited by me:

Factor 1…reflected the view that the value of biodiversity does not depend on its current usefulness to humans, potential future values to humans, or its importance to human survival.

• In terms of strategies and actions for conservation, the factor focused on global issues, such as changing human population growth rate and to a lesser extent changing the consumption levels of the wealthy.
• At the local level the factor did not express that conservation has a role in addressing poverty alleviation and considered it important to understand how people and nature interact in particular places, which suggests respondents considered that livelihoods of the poor as well as the rich are linked to biodiversity conservation.
• Because the focus of this factor was human population size and resource consumption, respondents appeared to be influenced by the concept of carrying capacity.

Factor 2…reflected a preservationist viewpoint, that conservation should prevent the human caused extinction of species.

• Nevertheless, the views in this factor emphasized social issues in the practice of conservation, particularly understanding how people and nature interact in places and to a lesser extent ensuring that conservation does no harm to human communities and does not displace long-term residents.
• This emphasis and the fact that science driven approaches to priority setting were rejected, suggests that this factor represents the viewpoint that conservation is mainly a political rather than a scientific endeavor.
• In terms of practical strategies, those that adhered to this factor do not believe conservation should focus on protected areas, involve strict law enforcement, or keep areas free from human influence.
• Rather, adherents to this factor strongly supported changes in consumption by the rich, which are actions far removed from the local level of protected areas. At the same time, the factor does not suggest the sole purpose of conservation is human survival.
• The factor also reflects a deep engagement in pragmatic and economic approaches to conservation action. Thus, the viewpoint expressed by this factor was that conservation planning must be local, can involve trade-based
  strategies, and can use incentives.
• This factor also showed there was an interest in holistic solutions, that conservation should not be confined to key priorities or areas and conservation actions should not be focused only where they are most cost-effective.

Factor 3…reflected a viewpoint that emphasized the diverse values of biodiversity, particularly the right of all species to exist and the role of species
in sustaining ecosystem functions

• The notions that trade in wild species can be a tool for conservation and that conservation action should prioritize cost-effectiveness were strongly rejected.
• Instead, priority was given to conservation of species and ecosystems, and the belief was that they should be conserved through implementation of protected areas. Little attention was given to the context and complexities of the practice of conservation, and there was a sense of disconnection between people and their environment at a variety of spatial scales, as evidenced by the focus on protected areas, little emphasis (relative to the other discourses) on understanding how people and nature interact, and rejection of any connection between conservation and consumption by the rich.
• Overall, this factor emphasized reasons biodiversity should be conserved, but gave little attention to mechanisms for achieving this goal.

Factor 4… reflected a view that biodiversity is useful to people, rejecting notions that biological diversity should be conserved for its beauty and that
all species have a right to exist.

• It emphasized the importance of connections between people and the environment, arguing that conservation success requires substantial changes in both human population growth and consumption by the rich.
• Conservation planning was seen to require detailed place-specific knowledge of human–environment interactions and not less-grounded patterns generated through tools such as GIS.
• The position expressed in this factor on economic tools was cautious: incentives are needed and cost-effectiveness is important, but trade in wild species and products was not considered a useful tool for biodiversity conservation.

There are several things I like about this article:

First is the notion that conservation is as political as it scientific— informed by the social sciences (policy, economics, sociology, psychology) and humanities (ethics, history) and ultimately debated by our local, national, and global societies.  It is not the role of science to drive contested, normative debates, although it’s great at providing information to inform these debates.

Second, now you see part of the reason why issues like conservation can be so contentious. There are myriad ways that people value biodiversity and it’s often difficult to reconcile these opposing philosophical positions.

Third, as I have written about previously on the blog, this is a good example of why nature needs to be situated in the context of culture and vice versa in order for challenging environmental problems to be studied effectively, as the authors allude to here (emphasis added):

[O]ur results provide an empirical challenge to the portrayal of conservation as a monolithic activity, driven by a convergent set of Western values, implicitly denying the possibility of differences in viewpoints about conservation at many spatial and temporal scales. The monolithic conception of conservation is based on an assumption that conservation professionals share a core set of values and goals, regardless of the social and economic contexts in which they are embedded and the experiences that have shaped their conservation interests. In reality, most conservation professionals draw on a range of values, from the intrinsic values of species to the use values of nature to humans. We consider it likely that such diverse views exist across a wide range of individuals and organizations involved in conservation.

…We believe conservation science and practice should not try to create a consensus under which conservation professionals can unite and instead acknowledge the diversity of opinions in the field. By acknowledging different
viewpoints, we believe conservation actors can build more honest and ultimately effective relationships with each other and the wider public.

SANDBROOK, C., SCALES, I., VIRA, B., & ADAMS, W. (2010). Value Plurality among Conservation Professionals Conservation Biology DOI: 10.1111/j.1523-1739.2010.01592.x

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Photo credit: wildxplorer

In a fascinating new article in PLOS One (open access), Daniel Nettle asks why we see social gradients in preventative health behaviors:

People of lower socioeconomic position have been found to smoke more, exercise less, have poorer diets, comply less well with therapy, use medical services less, adopt fewer safety measures, ignore health advice more, and be less health-conscious overall, than their more affluent peers. Some of these behaviors can simply be put down to financial constraints, as healthy diets, for example, cost more than unhealthy ones, but socioeconomic gradients are found even where the health behaviors in question would cost nothing, ruling out income differences as the explanation.

As we often assume with environmental or nutritional issues, maybe simply helping to better educate people is all that’s needed? Probably not, as Nettle points out, and with an interesting twist:

Socioeconomic gradients in health behavior are not easily abolished by providing more information. Informational health campaigns tend to lead to greater voluntary behavior change in people of higher socio-economic position, and thus can actually increase socioeconomic inequalities in health, even whilst improving health overall. Thus, we are struck with what we might call the exacerbatory dynamic of poverty: the people in society who face the greatest structural adversity, far from mitigating this by their lifestyles, behave in such ways as to make it worse, even when they are provided with the opportunity to do otherwise.

What are some of the possible explanations for this pattern, and are they sufficient?

Underlying socioeconomic differences in health behavior are differences in attitudinal and psychological variables. People of lower socioeconomic position have been found to be more pessimistic, have stronger beliefs in the influence of chance on health, and give a greater weighting to present over future outcomes, than people of higher socioeconomic position. These explanations seem clear.

However, they immediately raise the deeper question: why should pessimism, belief in chance, and short time perspective be found more in people of low socioeconomic position than those of high socioeconomic position? These deeper questions are at the level which behavioral ecologists call ultimate, as opposed to proximate causation

To develop more of an ultimate explanation, Nettle hypothesized that lower socioeconomic groups are subject to greater hazard or environmental harm or even simply the perception of living a more hazardous life.  This, in turn, discourages healthy behavior.

To test this hypothesis, he developed a mathematical/statistical model predicting the probability of dying in a given year, which is a combination of extrinsic risks that people cannot control as well as intrinsic risks that they can control through modified health behavior.   Thus, people choosing to take the time to engage healthier opportunities reduce their mortality risk.  Now there’s a tradeoff, however, because the more time people choose to undertake healthy behavior, the less time is left over for leisure activities and other life events.

Overall survival is therefore a combination of all of these factors, which can easily be modeled by assuming a range of values for time spent on health vs. other activities to see what kinds of mortality outcomes arise.

Here are the interesting results he found…

If it is the case that lower socioeconomic position is associated with a greater rate of extrinsic hazards (an assumption which needs justifying, see below), then we should expect people to respond to lower socioeconomic position with reduced preventative health behavior, because the benefits of that behavior to them are indeed lessened. This would in turn make their health outcomes worse, and so the gradient in health outcomes should in general be steeper than the underlying gradient in extrinsic risk exposures. Thus, the observed pattern of substantial socioeconomic gradients in health, which are to a  significant extent mediated by differences in health behavior, is exactly what we would predict if people are behaving adaptively given the environment in which they live.

Previous research on social inequalities in health behavior has found that people faced with socioeconomic deprivation endorse a greater belief in the influence of chance on life outcomes, particularly in the domain of health, are more pessimistic, and devalue future outcomes relative to present ones more sharply, than people of higher socioeconomic position. The model presented here is not in any sense an alternative to these accounts. On the contrary, the model here suggests an ultimate reason why these proximal psychological patterns might persist, and the proximal psychological accounts suggest how the adaptive behavior might actually be delivered. Clearly, people do not perform exact actuarial calculations in deciding whether to adopt a particular health behavior. Instead, they presumably employ some simple evolved heuristics. In this case, these might include something like ‘to the extent you see bad and unpredictable health outcomes besetting your peers, worry about today rather than tomorrow’.

And here’s where the environmental link comes in:

Several lines of evidence suggest that the assumption that lower socioeconomic position is associated with a greater degree of extrinsic hazard may not be unreasonable. First, studies of health inequalities generally find that controlling for behavioral factors (smoking, diet, etc.) attenuates socioeconomic gradients in health outcomes, but does not abolish them entirely. Of course, this could simply mean that not enough controls have been included, but it could also suggest that there is a residuum of health hazard which is extrinsic and thus not responsive to individuals’ behavioral decisions. Second, there are some health risk factors whose spatial distribution is socioeconomically patterned, and which people living in more deprived areas can do very little to avoid save for not living there. The clearest examples are noise, lead, and air pollution in the form of fine particles and nitrogen oxides. The levels of these hazards are higher in poor neighbourhoods, and their effects on morbidity and mortality well established. Third, many studies have found effects of living in poor neighbourhoods on health outcomes, above and beyond the effects of individual level socioeconomic characteristics. For example, poorer neighbourhoods are associated with substantially increased chances of accidental death or homicide, and heart disease, even once individual characteristics are adjusted for. This suggests that there are hazards fundamentally associated with living in these areas, which affect whoever it is that lives there.

In general, the model presented here draws the focus of health policy away from merely providing information or exhorting behavioral change, and onto extrinsic mortality. As with other neo-material approaches to health inequalities, it reminds us of the need to address the fundamental economic inequities which mean that some neighbourhoods contain higher risks of pollution, toxicity, and accident than others. More specifically, it suggests that reducing these structural inequities will reap a double dividend. It will have a primary effect on mortality inequality, and also a secondary effect as people respond to the primary effect by increasing their health-promoting behavior. Indeed, the secular trend in health behavior amongst middle-class people could be interpreted in this way. As economic development has eliminated many of the uncontrollable sources of danger, individuals have increased their investment in behaviors that mitigate those risks which do respond to individual choice. We need to create a similar dynamic in the most disadvantaged areas.

However, whilst changing structural conditions is the most important priority, the model also suggests that it is worth paying attention to people’s perceptions of extrinsic mortality. That is, in poor communities, individuals may perceive the local environment to be extrinsically dangerous to a greater extent than is in fact true (for example, because they are affected by social stereotypes or media portrayals). The model suggests that the psychological mechanisms which underlie behavioral decisions should be responsive to perceived levels of extrinsic mortality. If these perceptions are unrealistic, then they may lead to excessive fatalism and consequent disinvestment in health behavior. Thus, researchers and practitioners could usefully examine the genesis and malleability of people’s perceptions of the extrinsic dangers of their environments, and the relationships of these to their health attitudes and health behaviors.

What I love about this article is how it situates problems of sociology, psychology, public health, and justice squarely in the context of the environment—both actual and perceived.  And it encourages those of us interested in public health and well being to borrow a page from people engaged in environmental justice and just sustainability initiatives.

I also like how this result dismantles traditional notions of environmentalism and public health and forces us to consider new ways of studying pervasive problems in our world, where environmental studies scholars collaborate more with sociologists, psychologists, and historians to understand the ultimate causes of linked social-environmental challenges.

Nettle, D. (2010). Why Are There Social Gradients in Preventative Health Behavior? A Perspective from Behavioral Ecology PLoS ONE, 5 (10) DOI: 10.1371/journal.pone.0013371

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Photo credit: postopp1

In 40 years, there will be about 3 billion additional people living on the Earth (~9.5 billion total).   With all of these new folks, it’s easy to think about the added demands of energy, food, and water required to sustain their lifestyles.  And in terms of climate warming, it’s hard to escape the fact that significantly greater energy consumption will lead to rising rates of carbon emissions, unless there’s a shift to decarbonize the economy.

In this week’s early Edition of the Proceedings of the National Academy of Sciences (open access), Brian O’Neill and colleagues note that emissions are not just controlled by the sheer size of the human population but also by important demographic changes.

For example, how might an aging or more urban population affect emissions?  How about changes in household size?  Modelers of carbon emissions don’t usually ask these kinds of questions, so the conventionally projected emissions might be off if these additional demographic details matter.

The researchers developed a global economic model (Population-Environment-Technology, or PET) in which they specified relationships between demographic factors like houshold size, age, and urban/rural residency and economic factors like the demand for consumer goods, wealth, and the supply of labor.  Here’s a bit more on how this works:

In the PET model, households can affect emissions either directly through their consumption patterns or indirectly through their effects on economic growth in ways that up until now have not been explicitly accounted for in emissions models. The direct effect on emissions is represented by disaggregating household consumption for each household type into four categories of goods (energy, food, transport, and other) so that shifts in the composition of the population by household type produce shifts in the aggregate mix of goods demanded. Because different goods have different energy intensities of production, these shifts can lead to changes in emissions rates. To represent indirect effects on emissions through economic growth, the PET model
explicitly accounts for the effect of (i) population growth rates on economic growth rates, (ii) age structure changes on labor supply, (iii) urbanization on labor productivity, and (iv) anticipated demographic change (and its economic effects) on savings and consumption behavior.

Although there are some exceptions, households that are older, larger, or more rural tend to have lower per capita labor supply than those that are younger, smaller, or more urban. Lower-income households (e.g., rural households in developing countries) spend a larger share of income on food and a smaller share on transportation than higher-income households. Although labor supply and preferences can be influenced by a range of nondemographic factors, our scenarios focus on capturing the effects of shifts in population across types of households.

To project these demographic trends, we use the high, medium, and low scenarios of the United Nations (UN) 2003 Long-Range World Population Projections combined with the UN 2007 Urbanization Prospects extended by the International Institute for Applied Systems Analysis (IIASA) and derive population by age, sex, and rural/urban residence for the period of 2000–2100.

What did they find?

Although a shift to older and more urban household types occurs in all regions, changes in urbanization levels are most pronounced in China, sub-Saharan Africa, and the ODC [Other Developing Countries] region. Changes in household age strongly affect the European Union (EU) and other industrialized countries (OIC) regions as well as Latin America. Household size changes are largest in India, ODC, and Latin America.

….Results show that the effects of changes in population composition can have a significant influence on emissions in particular regions, separate from the effect of changes in population size. Aging can reduce emissions in the long term by up to 20%, particularly in industrialized country regions. Aging affects emissions in the PET model primarily through its influence on labor supply. In the model, aging populations are associated with lower labor productivity or labor force participation
rates at older ages, which (ceteris paribus) leads to slower economic growth. In contrast, urbanization can lead to an increase in projected emissions by more than 25%, particularly in developing country regions, also mainly through effects on labor
supply. The higher productivity of urban labor evident in the household surveys implies that urbanization tends to increase economic growth. Although other studies find that, controlling for income, urban living can be more energy efficient, survey data for urban households include income effects and therefore result in increased emissions.

In most regions, changes in household size have little additional effect on emissions beyond those already captured by aging (older households are also typically smaller). This result could be because of limitations in our household projections, which include household size changes driven by aging and urbanization but only capture the effects of behavioral change on household size in China and the United States. In China, reduced household size leads to lower emissions, a direction of influence counter to previous results. The reduction is driven primarily by the fact that large
households in older age categories typically have greater per capita labor supply (and income) than smaller households, because they include adult children of working age. Thus, aging, combined with a decline in household size, leads to a reduction in
per capita labor supply as older households become composed primarily of the elderly.

And the overall take-home message on emissions reductions?

[R]educed population growth could make a significant contribution to global emissions reductions. Several analyses have estimated how much emissions would have to be reduced by 2050 to meet long-term policy goals such as avoiding warming of more than 2 °C or preventing a doubling of CO2 concentrations through implementation of a portfolio of mitigation measures characterized as “stabilization wedges”. Our estimate that following a lower population path could reduce emissions 1.4–2.5 GtC/y by 2050 is equivalent to 16–29% of the emission reductions necessary to achieve these goals or approximately 1–1.5 wedges of emissions reductions. By the end of the century, the effect of slower population growth would be even more significant, reducing total emissions from fossil fuel use by 37–41% across the two scenarios.

O’Neill, B., Dalton, M., Fuchs, R., Jiang, L., Pachauri, S., & Zigova, K. (2010). Global demographic trends and future carbon emissions Proceedings of the National Academy of Sciences, 107 (41), 17521-17526 DOI: 10.1073/pnas.1004581107

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Photo credit: adwriter

When we think of human population change and resource use, it’s easy to assume that more people will consume more resources, such as water, energy, and food. An important corollary is that resource limitations will limit population growth.  Thomas Malthus was perhaps the most influential proponent of this idea.

However, several factors complicate this story:

(1) Affluence is a multiplier such that more people in a wealthy, high-consumption society lead to a disproportionate use of resources compared to people in poor countries. As my recent article on global change in Nature Knowledge shows,

the populations of China and India are roughly 1.32 and 1.14 billion people, respectively — about four times that of the US. However, the energy consumption per person in the US is six times larger than that of a person in China, and 15 times that of a person in India. Because the demand for resources like energy is often greater in wealthy, developed nations like the US, this means that countries with smaller populations can actually have a greater overall environmental impact. Over much of the past century, the US was the largest greenhouse gas emitter because of high levels of affluence and energy consumption. In 2007, China overtook the US in terms of overall CO₂ emissions as a result of economic development, increasing personal wealth, and the demand for consumer goods, including automobiles.

(2) Interestingly, resource limitations may actually inhibit our ability to slow population growth.  Yes, you read that right.  A new paper by John DeLong and colleagues in this week’s PLOS One (open access) argues exactly this.  Here’s why:

Influential demographic projections suggest that the global human population will stabilize at about 9–10 billion people by mid-century. These projections rest on two fundamental assumptions. The first is that the energy needed to fuel development and the associated decline in fertility will keep pace with energy demand far into the future. The second is that the demographic transition is irreversible such that once countries start down the path to lower fertility they cannot reverse to higher fertility. Both of these assumptions are problematic and may have an effect on population projections. Here we examine these assumptions explicitly. Specifically, given the theoretical and empirical relation between energy-use and population growth rates, we ask how the availability of energy is likely to affect population growth through 2050. Using a cross-country data set, we show that human population growth rates are negatively related to per-capita energy consumption, with zero growth occurring at ~13 kW, suggesting that the global human population will stop growing only if individuals have access to this amount of power. Further, we find that current projected future energy supply rates are far below the supply needed to fuel a global demographic transition to zero growth, suggesting that the predicted leveling-off of the global population by mid-century is unlikely to occur, in the absence of a transition to an alternative energy source. Direct consideration of the energetic constraints underlying the demographic transition results in a qualitatively different population projection than produced when the energetic constraints are ignored. We suggest that energetic constraints be incorporated into future population projections.

I love these kinds of unexpected outcomes that make us think more critically about simplified assumptions when it comes to the drivers and impacts of global change.

DeLong, J., Burger, O., & Hamilton, M. (2010). Current Demographics Suggest Future Energy Supplies Will Be Inadequate to Slow Human Population Growth PLoS ONE, 5 (10) DOI: 10.1371/journal.pone.0013206

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Photo credit: wili_hybrid

There have traditionally been two ways to produce more food for an increasing population:  Convert native ecosystems like forests and grasslands to agricultural fields (what we call “extensification”) or make the yields on existing croplands go up, through the use of things like machinery, fertilizers, irrigation, pesticides, and GMOs (what we call “intensification”).

Historically, these processes have occurred in tandem:  an initial phase of extensification and land clearing followed by development and intensification.  Converting North America’s prairies to corn and wheat in the 19th century is a classic example of the former, whereas 20th-century rise of fossil fuels, and the machines and fertilizer they support, is an example of the latter.

So while it’s not surprising to learn that developing nations in tropical regions are experiencing significant deforestation for food production, as Holly Gibbs and colleagues at Stanford describe in the early edition of the Proceedings of the National Academy of Sciences (citations removed for clarity), it’s important to understand the magnitude of ecosystem change as well as the drivers of change:

This study confirms that rainforests were the primary source for new agricultural land throughout the tropics during the 1980s and 1990s. More than 80% of new agricultural land came from intact and disturbed forests. Although differences occur across the tropical forest belt, the basic pattern is the same: The majority of the land for agricultural and tree plantation expansion comes from forests, woodlands, and savannas, not from previously cleared lands.

Worldwide demand for agricultural products is expected to increase by ∼50% by 2050, and evidence suggests that tropical countries will be called on to meet much of this demand. Consider, for example, that in developed countries the agricultural land area,
including pastures and permanent croplands, decreased by more than 412 million ha (34%) between 1995 and 2007, whereas developing countries saw increases of nearly 400 million ha (17.1%). Moreover, developing countries expanded their permanent croplands by 10.1% during the current decade alone, while permanent cropland areas in developed countries remained generally stable. If the agricultural expansion trends documented here for 1980–2000 persist, we can expect major clearing of intact and disturbed forest to continue and increase across the tropics to help meet swelling demands for food, fodder, and fuel.

Indeed, recent studies confirm that large-scale agro-industrial expansion is the dominant driver of deforestation in this decade, showing that forests fall as commodity markets boom. Rising commodity prices have been implicated in the destruction of Amazonian rainforests for soy production and peat swamp forests for oil palm production in Southeast Asia. Drivers of cropland expansion may impact forests directly through local or regional demand or indirectly through more globalized demand that may occur via market-mediated effects. Although this study does not specifically assess displacement or indirect land use changes, it does highlight the likelihood that intact and degraded forests will be replaced by agricultural land when such changes occur. Regardless of the mechanism, concern continues to mount about the large emissions of carbon dioxide that result when tropical forests are felled and often burned to make room for new agricultural land.

This was more of a land use change analysis, so it didn’t include a lot on the global drivers causing deforestation.  It would be a mistake, for instance, to ascribe all of this change to population growth in these tropical regions or efforts to supply more food to people living there.  Rather, extensification today is a global phenomenon driven by international trade, as the developing world loses native ecosystems to feed other countries.  And destroying forests and peatlands is a major net source of greenhouse gas emissions, so we’re also warming climate as an unintended consequence.

Why not just halt extensification and switch to intensification on existing farmland?  It’s expensive—moreso than simply clearing more land in many cases.  When the demand for cheap food rules the world, forest clearing in poor countries with abundant, cheap land is often what you get.

It should make us all pause considering that the environmental effects of the demand for goods like soy and palm oil by the industrialized world are being externalized to tropical countries.  We are now chopping down tropical forests to make soy burgers, biodiesel, and snack foods.  As Cameron Scott notes, “The Amazon, It’s What’s for Dinner.”

Reference:

H. K. Gibbs, A. S. Ruesch, F. Achard, M. K. Clayton, P. Holmgrene, N. Ramankutty, and J. A. Foley (2010). Tropical forests were the primary sources of new agricultural land in the 1980s and 1990s Proceedings of the National Academy of Sciences

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Photo courtesy of leoffreitas

I remember driving on a freeway in Phoenix after midnight in 1990.  The temperature was a cool 102 degrees F after breaking the all-time heat record of 126 F that day.  Deserts are good at cooling off at night.  But with all of the built environment in Phoenix storing heat from the day, the sidewalks, roads, and even swimming pools felt like they were being heated.

We all have probably experienced urban heat islands—the mass of dark asphalt and concrete absorbing solar radiation and radiating it back to space as heat.  The lack of water exacerbates the situation because there is little-to-no evaporative cooling.  Waste heat from cars, machines, air conditioners, and even human bodies also heat up the air.  And the warmer it gets, the stronger the tendency to crank up the air conditioners, generating even more waste heat.

The problem is potentially large in areas like the Middle East, India, parts of Africa, and the American Southwest, where rapid urbanization in warm, dry environments has the potential to make some urban areas much warmer at night than surrounding rural areas.

In a forthcoming article in Geophysical Research Letters¹, Mark McCarthy and colleagues at the Met Office, Hadley Centre, UK used a climate model that examines what climate might look like in a doubled CO₂ world and calculates the added warming caused by urbanization and wasted heat.

Their results were eye-opening:

• Urban regions in places like the Arabian Peninsula, Iran, and India may experience night time warming by as much as 3-5 degrees C above and beyond that caused by doubled CO₂ alone.
• The number of hot nights per year (defined as temperatures in the 99th percentile of nonurban areas) increase in the following cities:
  • London: 1-2 hot nights now vs. up to 10 hot nights in 2050
  • Sydney: 1-2 hot nights now vs. up to 15 hot nights in 2050
  • Delhi: 5-10 hot nights now vs. up to 30 hot nights in 2050
  • Beijing: 3-6 hot nights now vs. up to 50 hot nights in 2050
  • Los Angeles: 8-12 hot nights now vs. up to 40 hot nights in 2050
  • Tehran: 20 hot nights now vs. up to 60 hot nights in 2050
  • Sao Paulo: <5 hot nights now vs. up to 80 hot nights in 2050
  • Lagos (Nigeria): <5 hot nights now vs. up to 150 hot nights in 2050

As mentioned in an earlier post, we only need to remember Chicago in 1995 to recall the deadly impact that heat waves can have on urban people.  And as we saw in that unfortunate example, the victims were disproportionately the elderly and African American.

Although we may not be able to mitigate this warming, basic adaptation steps should be set into motion, including re-thinking urban design, making cities more resilient to hot environments, developing better energy and technology solutions (including cooling), installing green roofs, and putting into place emergency disaster plans and social safety nets for vulnerable populations.

¹Mark McCarthy, Martin Best, and Richard Betts (2010). Climate change in cities due to global warming and urban effects Geophysical Research Letters : 10.1029/2010GL042845

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The issue of land use change is a complex, with many factors being important historically, such as

• population growth (more land required for more people)
• technology (e.g., automobiles made suburban expansion feasible)
• economics (cheaper land and rents in suburbs compared to cities)
• policy (things like 30-yr mortgages, mortgage insurance, and FHA loans had a large impact on urban sprawl because they often made it cheaper to own rather than rent)
• cultural values (the romanticized notion of a detached home in a safe, pollution-free neighborhood with good schools)

In this week’s PLoS One, Robert McDonald and colleagues¹ examined land use change for 274 metro areas (figure 1) in the U.S. to determine tends across cities.

Their results were interesting (excerpts):

• 1.4 million ha of open space was lost, and the amount lost in a given city was correlated with population growth.
• American cities vary by more than an order of magnitude in their MSA-wide per capita land consumption. Generally large cities have small per capita land consumption, with the five smallest in 2000 being New York (459 m²/person), Miami (476 m²/person), Philadelphia (519 m²/person), Los Angeles (535 m²/person), and Washington, DC (536 m²/person). Conversely, many small cities have large per capita land consumption, with the five biggest in 2000 being Grand Forks, ND (5394 m²/person), Bismark, ND (3913 m²/person), Flagstaff, AZ (3381 m²/person), Enid, OK (3249 m²/person), and Cheyenne, WY (3073 m²/person).
• The per capita land consumption (m²/person) of most cities decreased on average over the decade from 1,564 to 1,454 m ²/person, but there was substantial regional variation and some cities even increased.
• Cities with greater conservation funding or more reform-minded zoning tended to decrease in per capita land consumption (scroll to table 1) more than other cities.
• The inequality of land consumption varied geographically, with less inequality on the East Coast compared to the West Coast (scroll to figure 4).

They provide a simplified snapshot of how development changes with history and geography (for a more-thorough yet readable treatment of land use in the U.S., check out Crabgrass Frontier by Kenneth Jackson):

The process of development plays out differently in cities with different socioeconomic histories. Moreover, cultural differences exist among and within many U.S. cities, leading to varying spatial patterns of development. However, a general historical pattern exists. In many U.S. cities, an urban core existed in the decades or centuries prior to the widespread use of the automobile, and these neighborhoods have high population density and small amounts of developed area per capita. The surrounding suburban and exurban areas, created predominately after WWII, contain residents living at lower population density and consume more land per capita. There are substantial economic links between these two zones, and in contemporary U.S. cities commuting occurs in both directions. Northeast U.S. cities that developed before the automobile typically follow this narrative. Many have a relatively dense urban core, but have adopted zoning policies that ensure contemporary suburban settlements occur at lower density. While they remain dense compared to other U.S. cities, they are getting less dense over time, as proportionally more of the population is in suburban areas. The declining manufacturing cities of the Rust Belt and the Southern Appalachians are an extreme example of this spreading out of population.

Southeastern U.S. cities, excluding Florida, are often newer and have less of a legacy of a dense urban core. They do not appear to be getting markedly denser, and the relatively fast population growth of these cities implies that their total impact on natural habitat in coming decades will be large. In contrast to the Southeast, Western cities appear to be getting denser, including those that do not have a historical legacy of a dense urban core such as Phoenix. These Western cities are often still growing quickly and consuming a great deal of land, but contemporary development is making these cities denser than they were previously. Many of these Western cities have a strong conservation culture, and the degree of conservation funding and reform-minded zoning correlates with how much denser they are getting. However, it should be noted that contemporary development in Western cities is still well below the densities found in the dense urban core of Northeastern U.S. cities, posing problems for designing effective public transit systems.

¹McDonald, R., Forman, R., & Kareiva, P. (2010). Open Space Loss and Land Inequality in United States’ Cities, 1990–2000 PLoS ONE, 5 (3) DOI: 10.1371/journal.pone.0009509

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In this week’s special issue devoted to food security, Science asks what it will take to feed 9 billion people by mid century.

Food insecurity—the inability of people to feed themselves—may rise if food supply cannot keep pace with population.  This is a concern that goes back over 200 years to Thomas Malthus.

One theme shows up in a few articles:  Can reducing meat consumption help in the battle to feed more people?

Erik Stokstad’s news feature (subscription required)¹ provides a nice lead:

The United States, for instance, has just 4.5% of the world’s population but accounts for about 15% of global meat consumption. Americans consume about 330 grams of meat a day on average—the equivalent of three quarter-pound hamburgers. In contrast, the U.S. Department of Agriculture recommends that most people consume just 142 to 184 grams of meat and beans daily. In the developing world, daily meat consumption averages just 80 grams. Those numbers suggest that people living in the United States and other wealthy nations could increase world grain supplies simply by forgoing that extra burger or chop.

However, he interviews researchers and cites studies that raise a number of issues potentially complicating this story…

• As meat consumption in developed nations decreases, the price of meat should decrease and become more affordable to people in the developing world, which could actually cause increased demand and meat consumption to rise globally by 13%.
• A study by the International Food Policy Research Institute’s Mark Rosegrant suggests that grain consumption would only rise slightly in the developing world.  As Stokstad reports,

Surprisingly, however, when the rich halved their meat habit, the poor didn’t necessarily get that much more grain—their largest source of calories. According to the model, per capita cereal consumption in developing nations rose by just 1.5%. That’s enough grain to ease hunger for 3.6 million malnourished children—but nowhere near the kinds of gains many expect from curbing meat consumption.

Stokstad argues the reason for this is a mismatch in grain fed to cattle vs. people.  In the developed world, for instance, farmers feed soybeans and corn to livestock, whereas people in developing nations in Asia eat more rice and wheat.  The gains in soybeans and corn therefore don’t necessarily translate into more food for people.

• When developed nations replace meat with pasta and bread, wheat prices worldwide rise, possibly threatening food insecurity to Asians who might no longer be able to afford the higher costs of wheat.

Following these assessments, Stokstad suggests

When all the pluses and minuses are added up, Rosegrant is confident that cutting meat consumption could ultimately help improve global food security. But “it’s a small contribution, like changing to fluorescent light bulbs” to fight global warming, he says.

While it’s important to consider unexpected twists and surprises, I’m not completely convinced by these arguments for a number of reasons, many of which Stokstad offers as caveats to the above assessment:

• As another review article by Charles Godfray et al.² in the same Science issue notes, about one third of the global supply of grain is fed to livestock.  That’s A LOT of food energy.  If you consider that you can feed many more people on a hectare of grain crops than livestock,  that’s a significant boost in food energy to the world, especially if the developed world cuts meat consumption significantly further than the 50% reduction Rosegrant assumes.   Some might argue that this is impractical—the world would never go vegetarian en masse.  Maybe so.  However, if the question is can we feed significantly more people by reducing meat consumption, the answer is clearly yes.  Whether we actually chose to reduce meat consumption enough is another (normative) question.
• As Stokstad notes, in many regions of the developing world, like Latin America and Africa, corn is a dietary staple, so diverting these grains from livestock to people will add more crops to the global markets and drop prices, encouraging greater consumption by a sizable fraction of the developing world.
• What about Asians, who eat more rice and wheat?  Stokstad seems to believe that crop substitutions are unlikely:

It’s true that as demand for corn drops, some farmers might start growing wheat instead. In general, however, climate, soil, or water availability often limit a farmer’s ability to switch crops easily. Iowa soybean growers, for instance, can’t start growing rice, which requires heavy irrigation.)

Yeah, that’s true for rice, but Stokstad solves his own dilemma because Iowans and most of the rest of the American Midwest COULD grow wheat in areas where surplus corn is currently grown and fed to livestock (if the relative price of wheat vs corn incentivizes the substitution).   Now we have increased core staples (corn, soybeans, wheat) to all developing regions.

• Note that increased wheat production would also alleviate the purported shortage of wheat experienced by Asians as the developed world substituted more grain for meat.
• As the Godfray article points out, livestock also lead to significant increases in methane emissions.

Godfray also adds a few points why reducing meat consumption may not be a complete salvation:

• Meat types vary in their production efficiency, meaning that some meats like poultry require less energy and water than other meats like beef.  Better breeding might be able to increase efficiency even more.
• A lot for livestock are grass fed on marginal pastures on which we can’t grow grains.
• Livestock are often important for other things, like manure fertilizers, plowing and transportation

There’s something to be said about the first point.  As the last post shows, eating beef is more energy intensive than poultry.  The second point is irrelevant— just because some animals are raised on marginal lands doesn’t change the fact that we still feed one third of global grain supply to the remaining livestock.   Again, the point is not to say that we need to eliminate meat consumption just for the sake of it.  We are saying that we can recoup 33% of the grains fed to livestock not fed on grass.  That’s the issue.

Bottom line: I applaud the search for factors that could potentially complicate simple explanations.  That’s a good thing for anticipating unexpected surprises that could appear during a big shift like dramatically reducing meat consumption.  And to be fair, all of these articles acknowledge that reducing meat can be part of an overall strategy to feed more people.

However, the Science articles err on the side of being too dismissive of the impacts that reducing meat consumption can have.

Is this a dose of reality considering that people won’t reduce meat consumption that much?   Maybe.  But I’d rather see Science address what is possible in addition to what is probable.  What we mainly get is the latter.

¹Stokstad, E. (2010) Could Less Meat Mean More Food? Science 327: 810 – 811.
DOI: 10.1126/science.327.5967.810

²Godfray, H.C. et al. (2010) Food Security: The Challenge of Feeding 9 Billion People. Science 327: 812 – 818. DOI: 10.1126/science.1185383

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Photo credit:  http://www.flickr.com/photos/splorp/ / CC BY-NC-ND 2.0

Little good news is coming out of Haiti these days.   There’s a deep social-environmental history that needs to be explored to understand why crises like poverty, AIDS, mudslides, and this week’s earthquake have been so devastating to the Haitian people.

I have written a bit about this history for one of the book projects I’m working on.  Below are a few excerpts, but before reading further, please consider helping with the humanitarian relief for earthquake victims:

• Doctors Without Borders USA
• AmeriCares
• An additional list of aid agencies can be found at the NY Times.

When hurricane Jeanne swept across the Caribbean, flooding rains killed over 3,000 people in the small nation of Haiti. Only 18 people died in the Dominican Republic on the same island.  Haiti has one of the highest population densities in the Caribbean. Its 8.7 million inhabitants live on less than half the land occupied by 9.4 million Dominicans, so population density is roughly two times greater.  Puerto Rico’s population density is as high as Haiti’s, but only seven people died in the storm.

Why, if Haiti’s population size is similar to the Dominican Republic’s and population density is the same as Puerto Rico’s, did Haiti suffer such a devastating loss of life?  Some argue that the loss of forests, with their capacity to prevent soil erosion, was a main reason why so many people were killed: heavy rains let loose massive mudslides on deforested hillsides.¹

Deforestation of Haiti’s landscape for agriculture and the manufacture of charcoal have left only 3% of the land surface forested.² Charcoal, produced by cutting trees and slow burning them in mud pits, meets about 85% of energy needs as cooking fuel.³ We see a ravaged countryside today and are tempted to blame this on Haiti’s high population density.  What is not as apparent, however, is how environmental degradation stems from a legacy of colonial resource extraction, slavery, corrupt governments, foreign intervention, and choices about energy, agriculture, and industry.

The mudslides and mortality did not occur in surrounding countries, which have less poverty and deforestation.  In fact, forest area is actually increasing in countries like Puerto Rico and the Dominican Republic where economic growth is rapid. Puerto Rico’s forest cover, for example, has risen from less than 10% to more than 40% in the last 60 years.¹ These forests are recovering on abandoned farmland with the transition from agriculture to industry.

It is therefore too simplistic to blame Haiti’s high population density and consumption of forest resources for the current state of the environment.  Human population growth drives environmental change but is seldom the sole factor behind environmental problems.  Instead, we need to figure out how population changes go hand-in-hand with social, economic, and technological changes so that we can explain environmental impacts.  Understanding and solving environmental challenges often requires simultaneous attention to demographic, economic, political, technological, and cultural values.

Haiti’s indigenous inhabitants practiced subsistence-based agriculture of corn, yams and cassava until their Columbus-era enslavement and genocide. Later, French colonists planted sugar cane in the well-suited warm, wet climate, and developed large, labor-intensive plantations. Throughout the 1700s, France imported thousands of African slaves to Haiti each year such that there were half a million working in 1789. During the colonial period, Haiti’s population was seven times larger than the Dominican Republic’s, which carried forward in time. Haiti exported tens of thousands of tons of sugar and most of the lumber from its forests back to France. The heavy exploitation of land for timber and sugar took a toll on the environment because of widespread land clearing, but it made Haiti one of the most profitable colonies in the Caribbean.

After Haitian independence in the early 19^th century, the nascent government was unable to support its own people in developing cash crops for export. To re-establish trade and diplomatic relations with France, Haiti’s government was forced to pay reparations for land and slaves lost during the revolution.  As much as 80% of Haiti’s budget went to pay these reparations, driving Haiti into significant debt from which it has not yet fully recovered.⁴

Today, Haiti is the poorest country in the Western Hemisphere, with the lowest combination of lifespan, education, and standard of living of any country outside Africa.⁵ Demographic, social, and economic changes happening elsewhere in the Caribbean are not happening as rapidly in Haiti. The abject poverty in which 80% of the population exists deteriorates the country’s environmental and political conditions and constrains economic development.  People are forced to choose between life in urban slums and life as poor, small-scale, subsistence farmers.  More than a million Haitians have emigrated to the United States and elsewhere since 1950.

In recent decades, many Haitian farmers have abandoned agriculture in search of greater profits from supplying charcoal to large urban and rural populations. With the collapse of agricultural and industrial exports, an unemployment rate of 33%, and sliding deeper into poverty, Haitians are forced to destroy remaining forests for charcoal fuel production. Consumption of natural resources just to stay alive is contributing to degraded environmental conditions.

Fertility remains high in Haiti because of high rates of mortality. Maternal, infant, and child mortality rates are high:  Sixty-eight infants and 52 mothers die for every 1,000 live births each year, and the under-five child mortality rate is 123 children per 1,000.  Haiti also suffers from the highest incidence of HIV/AIDS in the Western Hemisphere (5.6% of the population). The leading causes of death are diarrhea, respiratory infections, malaria, tuberculosis, and HIV/AIDS—diseases that are preventable or treatable in more developed countries.  However, 40% of Haitians have no access to health care.⁶ Haiti’s unstable governance, poverty, and environmental degradation exacerbate this need for large families as a social safety net.⁷ This is why simple approaches of reducing fertility, such as government support for contraception, have largely failed in Haiti.

Thus, Haiti’s changes in population and economic welfare, from its subsistence-based land use pattern, to an exploitative resource-extraction system, to a poor society where wealth, industry, and commercial agriculture have pulled out of the country, are not characteristic of the economic pattern—in which increasing economic development begets increased welfare—experienced by much of the developed world over past centuries.

Haiti is battling not only mudslides and earthquakes, but a colonial legacy that has predisposed its people to one devastating crisis after another.

References and Further Reading:

¹Aide, T.M. and H.R. Grau (2004) Globalization, migration, and Latin American ecosystems. Science 305:1915-1916.

²Kaiser, J. (2004) Wounding Earth’s fragile skin. Science 304:1616-1618.

³Collie, T. (2003) We know that this is destroying the land, but charcoal is what keeps us alive. South Florida Sun-Sentinel

⁴Hallward, P. (2004) Option Zero in Haiti. New Left Review 27:23-47

⁵Diamond, J. (2005) Collapse; How Societies Choose to fail or Succeed. Penguin.

⁶Farmer, P. (2004) Political violence and public health in Haiti. New England Journal of Medicine 350:1483-1486.

⁷de Sherbinin, A. (1996)  Human Security and Fertility: The Case of Haiti. Journal of Environment and Development 5(1):28-45.

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