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The Three Horsemen of Riches: Plague, War, and Urbanization in Early Modern Europe
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How did Europe escape the “Iron Law of Wages?” We construct a simple Malthusian model with two sectors and multiple steady states, and use it to explain why European per capita incomes and urbanization rates increased during the period 1350–1700. Productivity growth can only explain a small fraction of the rise in output per capita. Population dynamics—changes of the birth and death schedules—were far more important determinants of steady states. We show how a major shock to population can trigger a transition to a new steady state with higher per-capita income. The Black Death was such a shock, raising wages substantially. Because of Engel’s Law, demand for urban products increased, and urban centers grew in size. European cities were unhealthy, and rising urbanization pushed up aggregate death rates. This effect was reinforced by diseases spread through war, financed by higher tax revenues. In addition, rising trade also spread diseases. In this way higher wages themselves reduced population pressure. We show in a calibration exercise that our model can account for the sustained rise in European urbanization as well as permanently higher per capita incomes in 1700, without technological change. Wars contributed importantly to the “Rise of Europe”, even if they had negative short-run effects. We thus trace Europe’s precocious rise to economic riches to interactions of the plague shock with the belligerent political environment and the nature of cities.
Key words: Malthus to Solow, Long-run Growth, Great Divergence, Epidemics, Demographic Regime
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How wide is a stream? Spatial extent of the potential ‘‘stream signature’’ in terrestrial food webs using meta-analysis
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The magnitude of cross-ecosystem resource subsidies is increasingly well recognized; however, less is known about the distance these subsidies travel into the recipient landscape. In streams and rivers, this distance can delimit the ‘‘biological stream width,’’ complementary to hydro-geomorphic measures (e.g., channel banks) that have typically defined stream ecosystem boundaries. In this study we used meta-analysis to define a ‘‘stream signature’’ on land that relates the stream-to-land subsidy to distance. The 50% stream signature, for example, identifies the point on the landscape where subsidy resources are still at half of their maximum (in- or near-stream) level. The decay curve for these data was best fit by a negative power function in which the 50% stream signature was concentrated near stream banks (1.5 m), but a non-trivial (10%) portion of the maximum subsidy level was still found .0.5 km from the water’s edge. The meta-analysis also identified explanatory variables that affect the stream signature. This improves our understanding of ecosystem conditions that permit spatially extensive subsidy transmission, such as in highly productive, middle-order streams and rivers. Resultant multivariate models from this analysis may be useful to managers implementing buffer rules and conservation strategies for stream and riparian function, as they facilitate prediction of the extent of subsidies. Our results stress that much of the subsidy remains near the stream, but also that subsidies (and aquatic organisms) are capable of long-distance dispersal into adjacent environments, and that the effective ‘‘biological stream width’’ of stream and river ecosystems is often much larger than has been defined by hydro-geomorphic metrics alone. Limited data available from marine and lake sources overlap well with the stream signature data, indicating that the ‘‘signature’’ approach may also be applicable to subsidy spatial dynamics across other ecosystems.
Key words: aquatic subsidies; dispersal; distance; food webs; insects; meta-analysis; stream.
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How many tree species are there in the Amazon and how many of them will go extinct?
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New roads, agricultural projects, logging, and mining are claiming an ever greater area of once-pristine Amazonian forest. The Millennium Ecosystems Assessment (MA) forecasts the extinction of a large fraction of Amazonian tree species based on projected loss of forest cover over the next several decades. How accurate are these estimates of extinction rates? We use neutral theory to estimate the number, relative abundance, and range size of tree species in the Amazon metacommunity and estimate likely tree-species ex- tinctions under published optimistic and nonoptimistic Amazon scenarios. We estimate that the Brazilian portion of the Amazon Basin has (or had) 11,210 tree species that reach sizes >10 cm DBH (stem diameter at breast height). Of these, 3,248 species have population sizes >1 million individuals, and, ignoring possible climate-change effects, almost all of these common species persist under both optimistic and nonoptimistic scenarios. At the rare end of the abundance spectrum, however, neutral theory predicts the existence of 5,308 species with <10,000 individuals each that are expected to suffer nearly a 50% extinction rate under the nonop- timistic deforestation scenario and an 37% loss rate even under the optimistic scenario. Most of these species have small range sizes and are highly vulnerable to local habitat loss. In ensembles of 100 stochastic simulations, we found mean total extinction rates of 20% and 33% of tree species in the Brazilian Amazon under the optimistic and nonoptimistic scenarios, respectively.
Amazonian tree diversity neutral theory tropical tree extinction
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How complex do models need to be to predict dispersal of threatened species through matrix habitats?
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Persistence of species in fragmented landscapes depends on dispersal among suitable breeding sites, and dispersal is often influenced by the ‘‘matrix’’ habitats that lie between breeding sites. However, measuring effects of different matrix habitats on movement and incorporating those differences into spatially explicit models to predict dispersal is costly in terms of time and financial resources. Hence a key question for conservation managers is: Do more costly, complex movement models yield more accurate dispersal predictions? We compared the abilities of a range of movement models, from simple to complex, to predict the dispersal of an endangered butterfly, the Saint Francis’ satyr (Neonympha mitchellii francisci). The value of more complex models differed depending on how value was assessed. Although the most complex model, based on detailed movement behaviors, best predicted observed dispersal rates, it was only slightly better than the simplest model, which was based solely on distance between sites. Consequently, a parsimony approach using information criteria favors the simplest model we examined. However, when we applied the models to a larger landscape that included proposed habitat restoration sites, in which the composition of the matrix was different than the matrix surrounding extant breeding sites, the simplest model failed to identify a potentially important dispersal barrier, open habitat that butterflies rarely enter, which may completely isolate some of the proposed restoration sites from other breeding sites. Finally, we found that, although the gain in predicting dispersal with increasing model complexity was small, so was the increase in financial cost. Furthermore, a greater fit continued to accrue with greater financial cost, and more complex models made substantially different predictions than simple models when applied to a novel landscape in which butterflies are to be reintroduced to bolster their populations. This suggests that more complex models might be justifiable on financial grounds. Our results caution against a pure parsimony approach to deciding how complex movement models need to be to accurately predict dispersal through the matrix, especially if the models are to be applied to novel or modified landscapes.
Key words: capture–mark–recapture; connectivity; dispersal; habitat fragmentation; matrix habitat; Neonympha mitchellii francisci; restoration; spatially explicit individual-based simulation model.
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Reframing Environmental Messages to be Congruent with American Values
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Prior research has explored the relationship between values, attitudes about environmental issues, and pro-environmental behavior. These studies have shown a consistent pattern of results — individuals who value self-transcendent life goals tend to care more about environmental problems, favor environmental protection over economic growth, and engage in more proenvironmental behavior. In contrast, indi- viduals who value self-enhancing life goals tend to hold more egoistic concerns about environmental issues, tend to favor economic growth over environmental protection, and tend to engage in fewer environmental behaviors. Research on American values suggests that overall, people in the U.S. tend to hold strong self-enhancing values. These self- enhancing values have largely been considered incongruous with the values that lead to environmental concern and to environmental behavior. In this paper, we synthesize the past research on the relationship between values and environmen- tal behavior. Lessons from the Biodiversity Project are used to illustrate efforts to create effective value-based environmental messages.
Keywords: values, environmental attitudes, proenvironmental behavior, value-based messages
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Physically based assessment of hurricane surge threat under climate change
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Storm surges are responsible for much of the damage and loss of life associated with landfalling hurricanes. Understanding how global warming will affect hurricane surges thus holds great interest. As general circulation models (GCMs) cannot simulate hurricane surges directly, we couple a GCM-driven hurricane model with hydrodynamic models to simulate large numbers of synthetic surge events under projected climates and assess surge threat, as an example, for New York City (NYC). Struck by many intense hurricanes in recorded history and prehistory, NYC is highly vulnerable to storm surges. We show that the change of storm climatology will probably increase the surge risk for NYC; results based on two GCMs show the distribution of surge levels shifting to higher values by a magnitude comparable to the projected sea-level rise (SLR). The combined effects of storm climatology change and a 1 m SLR may cause the present NYC 100-yr surge flooding to occur every 3–20 yr and the present 500-yr flooding to occur every 25–240 yr by the end of the century.
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The increasing intensity of the strongest tropical cyclones
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Atlantic tropical cyclones are getting stronger on average, with a 30-year trend that has been related to an increase in ocean temperatures over the Atlantic Ocean and elsewhere1–4. Over the rest of the tropics, however, possible trends in tropical cyclone intensity are less obvious, owing to the unreliability and incompleteness of the observational record and to a restricted focus, in previous trend analyses, on changes in average intensity. To quantify and determine the significance of these trends, we use quantile regression. Quantile regression as employed here is a method to estimate the change (trend) in lifetime-maximum wind speed quantile as a function of year. A quantile is a point taken from ntensities that cyclones achieve during their lifetimes), estimated from homogeneous data derived from an archive of satellite records. We find significant upward trends for wind speed quantiles above the 70th percentile, with trends as high as 0.36 0.09 m s21 yr21 (s.e.) for the strongest cyclones. We note separate upward trends in the estimated lifetime-maximum wind speeds of the very strongest tropical cyclones (99th percentile) over each ocean basin, with the largest increase at this quantile occurring over the North Atlantic, although not all basins show statistically significant increases. Our results are qualitatively consistent with the hypothesis that as the seas warm, the ocean has more energy to convert to tropical cyclone wind.
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Atlantic hurricanes and climate over the past 1,500 years
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Atlantic tropical cyclone activity, as measured by annual storm counts, reached anomalous levels over the past decade1. The short nature of the historical record and potential issues with its reliability in earlier decades, however, has prompted an ongoing debate regarding the reality and significance of the recent rise2–5. Here we place recent activity in a longer-term context by comparing two independent estimates of tropical cyclone activity over the past 1,500 years. The first estimate is based on a composite of regional sedimentary evidence of landfalling hurricanes, while the second estimate uses a previously published statistical model of Atlantic tropical cyclone activity driven by proxy reconstructions of past climate changes. Both approaches yield consistent evidence of a peak in Atlantic tropical cyclone activity during medieval times (around AD 1000) followed by a subsequent lull in activity. The statistical model indicates that the medieval peak, which rivals or even exceeds (within uncertainties) recent levels of activity, results from the reinforcing effects of La-Nina-like climate conditions and relative tropical Atlantic warmth.
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Coupling snowpack and groundwater dynamics to interpret historical streamflow trends in the western United States
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A key challenge for resource and land managers is predicting the consequences of climate warming on streamflow and water resources. During the last century in the western United States, significant reductions in snowpack and earlier snowmelt have led to an increase in the fraction of annual streamflow during winter and a decline in the summer. Previous work has identified elevation as it relates to snowpack dynamics as the primary control on streamflow sensitivity to warming. But along with changes in the timing of snowpack accumulation and melt, summer streamflows are also sensitive to intrinsic, geologically mediated differences in the efficiency of landscapes in transforming recharge (either as rain or snow) into discharge; we term this latter factor drainage efficiency. Here we explore the conjunction of drainage efficiency and snowpack dynamics in interpreting retrospective trends in summer streamflow during 1950–2010 using daily streamflow from 81 watersheds across the western United States. The recession constant (k) and fraction of precipitation falling as snow (Sf) were used as metrics of deep groundwater and overall precipitation regime (rain and/or snow), respectively. This conjunctive analysis indicates that summer streamflows in watersheds that drain slowly from deep groundwater and receive precipitation as snow are most sensitive to climate warming. During the spring, however, watersheds that drain rapidly and receive precipitation as snow are most sensitive to climate warming. Our results indicate that not all trends in western United States are associated with changes in snowpack dynamics; we observe declining streamflow in late fall and winter in rain-dominated watersheds as well. These empirical findings support both theory and hydrologic modelling and have implications for how streamflow sensitivity to warming is interpreted across broad regions.
KEY WORDS streamflow trend; hydrologic processes; groundwater processes; climate; warming
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Land cover effects on runoff patterns in eastern Piedmont (USA) watersheds
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Physiography and land cover determine the hydrologic response of watersheds to climatic events. However, vast differences in climate regimes and variation of landscape attributes among watersheds (including size) have prevented the establishment of general relationships between land cover and runoff patterns across broad scales. This paper addresses these difficulties by using power spectral analysis to characterize area-normalized runoff patterns and then compare these patterns with landscape features among watersheds within the same physiographic region. We assembled long-term precipitation and runoff data for 87 watersheds (first to seventh order) within the eastern Piedmont (USA) that contained a wide variety of land cover types, collected environmental data for each watershed, and compared the datasets using a variety of statistical measures. The effect of land cover on runoff patterns was confirmed. Urban-dominated watersheds were flashier and had less hydrologic memory compared with forest-dominated watersheds, whereas watersheds with high wetland coverage had greater hydrologic memory. We also detected a 10–15% urban threshold above which urban coverage became the dominant control on runoff patterns. When spectral properties of runoff were compared across stream orders, a threshold after the third order was detected at which watershed processes became dominant over precipitation regime in determining runoff patterns. Finally, we present a matrix that characterizes the hydrologic signatures of rivers based on precipitation versus landscape effects and low-frequency versus high-frequency events. The concepts and methods presented can be generally applied to all river systems to characterize multiscale patterns of watershed runoff.
KEY WORDS watershed hydrology; power spectral analysis; hydrologic signatures; fluvial landscape ecology; hydrologic memory
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